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PUBLISHED PROJECT REPORT PPR726

Investigating the NeuroSky MindWave™ EEG Headset

R. Robbins and M. Stonehill

Prepared for: Transport Research Foundation

Project Ref:

Quality approved:

Andy Kirkham

(Project Manager) A Kirkham

Antony Whitmore

(Technical Referee) A Whitmore

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Disclaimer This report has been produced by the Transport Research Laboratory. The information contained herein is the property of TRL Limited. Whilst every effort has been made to ensure that the matter presented in this report is relevant, accurate and up-to-date, TRL Limited cannot accept any liability for any error or omission, or reliance on part or all of the content in another context.

When purchased in hard copy, this publication is printed on paper that is FSC (Forest Stewardship Council) and TCF (Totally Chlorine Free) registered.

Contents amendment record This report has been amended and issued as follows:

Version Date Description Editor Technical Referee

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Contents

1 Abstract 1

2 Introduction 2 2.1 The NeuroSky MindWave™ 2 2.2 Software 3 2.3 Aim of the study 4

3 Literature Review 5 3.1 Brief history 5 3.2 EEG 5 3.3 BCI 5 3.4 Relevant research studies 6

3.4.1 Fatigue 6 3.4.2 Workload 6 3.4.3 NeuroSky MindWave™ 7

3.5 NeuroSky research studies 8 3.5.1 Gaze tracking and NeuroSky 8 3.5.2 NeuroSky and Second Life 9 3.5.3 NeuroSky and human emotional response 9 3.5.4 NeuroSky and Meditation output 10 3.5.5 NeuroSky and Reading tasks 10 3.5.6 MIT Mind Controlled Cycle helmet 10

3.6 EEG and driving behaviour studies 10 3.6.1 Intention to brake 10 3.6.2 Predicting upcoming manoeuvres 11 3.6.3 Anticipation 11 3.6.4 Response to traffic lights 11

4 Methodology 12 4.1 Aim and objectives 12 4.2 Participants 12 4.3 Experimental design/procedure 12 4.4 Hypotheses 13

4.4.1 Hypotheses relating to attention and memory tests 13 4.4.2 Hypotheses relating to Relaxation CD 13 4.4.3 Hypotheses relating to self-completion questionnaire 14

5 Results 15


5.1 Physiological data 15 5.2 Physiological data 16

5.2.1 Distribution of the physiological data 16 5.2.2 Gamma (Hypothesis 1) 16 5.2.3 Beta 17 5.2.4 Meditation 18 5.2.5 Attention 18

5.3 Questionnaire results 20 5.3.1 Initial comfort level 20 5.3.2 Change in comfort level 20 5.3.3 Awareness of wearing device 21

6 Discussion 22 6.1 Usefulness of MindWave to future work 23 6.2 Future work 23

Appendix A - References 25 TEST 1: REY AUDITORY VERBAL LEARNING TEST (AVLT) 1 27 TEST 2: WAIS-R DIGIT SPAN 28


1 Abstract The NeuroSky MindWave™ is a device for monitoring electrical signals generated by neural activity in the brain. The device is worn on the head and consists of a headband, an ear-clip, and a sensor arm containing the EEG electrode which rests on the forehead above the eye (FP1 position). Compared to traditional EEG devices, it is inexpensive, simple to operate, and unobtrusive.

The aim of the study was to investigate how well the headset performed with regards to the quality and quantity of data it gathered in order to determine whether it could be more widely used as a cost-effective means of recording EEG output in a human factors research setting. This was achieved by instructing a sample of 24 participants to undertake six cognitive tasks whilst the output from the NeuroSky MindWave™ was recorded.

Overall, our data produced mixed results. Only the Relaxation measure produced changes in brain wave power which were consistent with our expectations (when participant’s relaxed their meditation power increased). All other tests were either non-significant or produced results which contradicted our expectation. Possible explanations for the observed results are expounded.

From the data gathered the usefulness of the MindWave device in a human factors research setting appears to be limited. However, the authors acknowledge that changes to the procedure and data processing might significantly improve the quality of the data gathered.

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2 Introduction

2.1 The NeuroSky MindWave™

The NeuroSky MindWave™ headset was launched in 2010/11 and has been designed to identify and monitor electric signals generated by neural activity in the brain. It complements the NeuroSky MindSet™ headset, released in 2009, which has been used to research ADHD, Alzheimer’s and Cognitive Stress (NeuroSky Inc., 2010). The MindWave™ consists of a headband, an ear-clip, and a sensor arm containing the EEG electrode which rests on the forehead above the eye (FP1 position, in accordance with the American Electroencephalographic Society’s (1994) 10-20 system of electrode placement).

The measurements of the MindWave™ are outlined as follows:

• Raw signal

• EEG power spectrum: Provides information on a user’s brainwaves (Delta, Theta, Alpha, Beta and Gamma).

• eSense meters for Attention and Meditation: Determines how effectively the user is engaging Attention (similar to concentration) or Meditation (similar to relaxation) by decoding the electrical signals and applying algorithms to provide readings on a scale of 0 to 100. These values are described in Table 1.

Table 1 – Descriptions of eSense meter values

Value Description

1-20 ‘Strongly lowered’ levels

20-40 ‘Reduced’ levels

40-60 ‘Neutral’ / ‘Baseline’ levels

60-80 ‘Slightly elevated’ / higher than normal levels

80-100 ‘Elevated’ / heightened levels

The eSense Attention meter indicates the intensity of a user’s level of mental ‘focus’ or ‘attention’ to determine levels of concentration. Distractions, wandering thoughts, lack of focus, or anxiety may lower the Attention meter level. The eSense Meditation meter is related to the active mental processes in the brain and indicates the intensity of a user’s level of mental ‘calmness’ or ‘relaxation’. Relaxing the body and closing one’s eyes often helps the mind to relax and increases the Meditation meter level. Distractions, wandering thoughts, anxiety, agitation, and sensory stimuli may lower the Meditation meter levels.

• eSense Blink Detection

• On-head detection

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2.2 Software

The software provided as part of the product comprises specially-designed neuroscience relaxation, mental fitness and game applications which measure brainwave signals and monitor attention levels during interaction. Table 2 describes these applications.

Table 2 – Applications included with the NeuroSky MindWave™ Software

Application Description

Meditation Journal

Keeps track of your meditation, attention, and brainwave recordings in a journal and presents them in a series of data charts and accomplishments.

SpeedMath

Trains your arithmetic skills through quick thinking maths questions. After completing a problem set, you can review your changing attention levels and hone in on your problem areas.

BlinkZone

Uses blink detection. The user blinks to set off a firework and the bigger the blink, the bigger the firework. You can try to set off many at once or try a game of maintaining your calm. The colour and height of the fireworks are based on involuntary brainwave activity.

Schulte

Used to monitor and train attention levels. There are 25 numbers within a 5×5 grid and the user should click the numbers in ascending numerical order. The completion chart evaluates the effectiveness of the training and the trends of the user’s attention level.

SpadeA

Used to optimize reaction time and pattern recognition. A mental card game where the user must watch the moving cards and keep their mind on the Ace of Spades. The completion chart evaluates the effectiveness of the training and the trends of the user’s attention level.

Find Number

An attention training tool whereby the player searches for the specified number. The game monitors the user’s attention in the background and the completion chart evaluates the trends of the user’s attention level.

MindHunter

A mind-controlled hunting game to build up the user’s concentration. Once the attention has reached a specified level, the user must blink their eyes to fire while the target is locked.

Man.Up

A meditation brain-game that uses mind-driven difficulty.

MindtyAnt

Uses a combination of keyboard and mind control to train the user’s ability of concentration.

Jack's Adventure

A problem solving game in which the user must raise their attention level so that the spaceship will plant trees on the planets.

(Source: http://store.neurosky.com/products, accessed October 2013)

In addition to the software provided there are also various applications available to download online, which include:

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http://store.neurosky.com/products


• Developer Tools (MDT): A collection of drivers, sample code, and documentation describing how to develop applications for several software platforms. It provides all the tools and resources necessary to create and publish games and applications.

• Research Tools (MRT): A collection of two data collection and viewing applications which enables researchers to use the MindSet or MindWave as a data collection device.

• Visualizer 2.0: Works with iTunes to enable the user to listen to music and watch how the on-screen shapes morph and change colour to show how their mind responds.

2.3 Aim of the study

This project aims to access potential uses of the NeuroSky MindWave™ in driver monitoring / behaviour and human factors studies. It will investigate how well the headset performs with regards to the quality and quantity of data it gathers.

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3 Literature Review

3.1 Brief history

The existence of electrical currents in the brain was first discovered in 1875 by an English physician, Richard Caton. In 1924, Hans Berger (a German scientist) recorded electrical brain activity and used the word electroencephalogram (EEG) to describe brain electric potentials in humans. He recognised that “...brain activity changes in a consistent and recognizable way when the general status of the subject changes from relaxation to alertness.” (Teplan, 2002). In 1934, Adrian and Matthews published a paper which verified the concept of ‘human brain waves’. During the 1950’s, EEG technology was increasingly aplied in neurology, neurosurgery, and cognitive science (Ali, 2012), and in 1964 Dr Grey Walter connected electrodes directly to the motor areas of a subject’s brain to record brain activity. Neuroscience research over the last decades has advanced to provide an increasing understanding of brain activity.

3.2 EEG

To measure brain activity, the electrooculogram (EOG) or the electroencephalograph (EEG) are usually used. EOG provides a measure of the difference in electrical activity between the cornea and the retina, and is primarily used when measuring eye movements such as eye blink rate and eye closure. In a paper by Miller in 2001, it was reported that limited research had been conducted into the benefits of using EOG for workload measurement. The most common type of electrophysiological indicator used for workload studies is the EEG. EEG is the recording of electrical brain activity through electrode sensors placed on the scalp, and EEG signals are classified into wave bands to indicate various states or activity levels. The different wave bands are outlined as follows:

• Gamma Waves (>30 Hz): Relate to some senses and memory.

• Beta Waves (13-30 Hz): Focus, active attention, thinking, problem-solving. Beta Waves dominate when the brain is aroused and mentally engaged in activities.

• Alpha Waves (8-13 Hz): Relaxation, meditation, non-arousal, relaxed awareness without any concentration. Alpha Waves can be induced by closing the eyes and relaxing, and abolished by opening the eyes or engaging the brain in activities such as thinking or calculating. In a study by Klimesch et al., it was found that low alpha frequencies relate to attention and that high alpha frequencies relate to some cognitive processes such as memory (Guðmundsdóttir, 2011).

• Theta Waves (4-8 Hz): Drowsiness, deep Relaxation, daydreaming

• Delta Waves (0.5-4 Hz): Deep sleep, unconsciousness.

EEG wave classification has been used to help diagnose sleep disorders. It is also used in the construction of brain computer interfaces (BCI’s) to assist disabled people with daily living tasks (Min et al, 2009).

3.3 BCI

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A brain computer interface (BCI) is a communication pathway which interprets the user’s command from their brainwaves to enable simple tasks to be carried out. BCI systems have primarily focused on assisting disabled people through neural prostheses to restore damaged hearing, sight and movement so that they can interact with their environment. However, more recent applications of BCI have involved the development of products for commercial purposes, such as video game controllers and mind-controlled cars (Ali, 2012). Stamps et al., 2010 have stated that: “Ironically, the only affordable (to the average income person) BCI systems commercially available target the media and PC gaming industry.”

Cester et al., 2011 describe visually evoked potentials (small changes in the EEG in response to visual stimuli which can be elicited by flashing lights) and the P300 (a positively deflected peak in the raw EEG signal which occurs approximately 300 milliseconds after the presentation of an unexpected stimulus). Both visually evoked potentials and P300 provide robust signals which can input into communication BCI applications such as keyboard typing or the moving of a wheelchair.

3.4 Relevant research studies

Neurophysiological research for safety-critical applications has mainly focused on the detection of fatigue and high mental workload (Haufe et al., 2011).

3.4.1 Fatigue

Hu et al., (2012) conducted a study to detect drowsiness based on EEG power spectrum analysis. A total of 40 subjects were deprived of sleep for one night and then asked to participate in a driving simulator experiment. Whilst driving, EEG and EOG were measured to determine the states of the drivers. The study found that 86% of the drivers’ drowsiness states could be accurately detected, and there was an identified need to develop an accurate monitoring system which can detect driving fatigue symptoms. Watling et al., (2012) conducted a study which investigated drivers’ perceptions of their levels of sleepiness. A total of 26 subjects woke at 5am and later took part in a driving simulator task, the Hazard Perception test. EEG measurements and subjective measures (sleepiness ratings) were recorded, and the participants were asked to identify when they believed their sleepiness had impaired their ability to drive safely. The EEG recordings were visually inspected for signs of sleep (alpha and theta activity, slow rolling eye movements and extended eye closures) and micro sleeps. It was found that all of the participants decided to cease driving and self-regulate their behaviour to take a break, however the levels of sleepiness achieved prior to driving cessation suggested poor accuracy in self-perception and regulation. Most participants ceased driving before one hour had elapsed, with the longest duration being 76 minutes.

3.4.2 Workload

Measurements of workload can be physiological (e.g. cardiac activity, brain activity, respiratory activity, speech measures and eye activity), subjective (e.g. rankings or scales to measure the amount of workload a person is feeling) or performance (e.g. measuring how well a subject performs in a task). Whilst the measurement of brain activity using EEG is a physiological measure, both subjective and performance measures can be used to supplement research findings.

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Miller (2001) refers to two EEG studies which assessed the impact of increasing mental workload on brainwaves. Both studies found that an increase in mental workload caused Alpha Waves to disappear or decrease, however the first study (Sabbatini, 1997) found that the Alpha Waves were replaced by Beta Waves, and the second study (Hankins & Wilson, 1998) found that the presence of Theta Waves increased.

Kohlmorgen et al. (2007) conducted a study to develop an EEG-based system which can detect high mental workload in drivers operating under real traffic conditions. A total of 17 subjects were asked to drive at approximately 100km/h on the public German highway B10 in moderate traffic conditions during the daytime and given workload-inducing tasks whilst driving. The subjects were instructed not to speak during the experiment, to avoid additional workload. It was found that mental workload detection in real-time and in real operating environments is possible and can lead to an improved performance of a subject. It was suggested that the mitigation of high mental workload can be of vital importance, since reaction times can be improved and braking distances can be reduced which could help to prevent a collision.

Kim et al. (2001) assessed the physiological and psychological responses of drivers to assess driver workload when driving on the highway. A total of 51 subjects took part in the study and their physiological data was analysed by relative power spectrum analysis, which provides an indicator of the power of specific frequency bands that are present in the underlying EEG signal. It was reported that a driver acts on an optimal level of workload or demand that yields the highest performance. In overload conditions, drivers showed excited EEG records during driving and more Beta Waves were apparent whereas in underload conditions, drivers showed relaxed EEG records.

Lei et al. (2009) conducted a study where participants were asked to perform a Lane Change Task (LCT) combined with a secondary auditory task (the Paced Auditory Addition Serial Task, PASAT) during a simulated driving assessment. EEG recordings were combined with performance data from the tasks in order to understand driver’s workload. It was found that changes in amplitudes of specific event-related potential (ERP) components, including P3b (a subcomponent of the P300 which is particularly sensitive to the cognitive demands of a task) could be directly used for representing driver’s mental workload.

3.4.3 NeuroSky MindWave™

To date, EEG studies have often emphasised the drawbacks of using existing EEG technology to measure neural activity in the brain. Guðmundsdóttir (2011) refers to it being a very time-consuming process because the preparation time to position the electrodes around the head and attach them with conductive gel takes so long, particularly when using high density electrode arrays. The plastic cap can take up to half an hour to fit and is reportedly rather uncomfortable to wear. In addition, the process must be supervised and performed by trained personnel.

Recent advances in EEG technology have led to the development of cheaper and easier to set up products which use dry electrode-based EEG hardware, such as the NeuroSky MindSet. The NeuroSky MindSet consists of a headband with three sensors. The reference and ground electrodes are clipped onto the earlobe, whilst the EEG recording electrode is positioned on the forehead. The sensors require no gel or saline for recording, and no expertise is required for set up. In a study referred to by Mostow et

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al., (2011), “Even with the limitations of recording from only a single sensor and working with untrained users, the MindSet distinguished two fairly similar mental states (neutral and attentive) with 86% accuracy.”

The NeuroSky MindSet reads electric signals generated by neural activity in the brain and decodes them by applying algorithms to provide readings on a scale of 0 to 100. It provides information on a user’s brainwaves (Delta, Theta, Alpha, Beta and Gamma) in order to determine levels of attention and relaxation. NeuroSky is marketed within the entertainment and gaming sector, particularly targeting the development of children’s mental capacities. According to Nijboer et al., 2011, NeuroSky has sold approximately 1 million MindSets.

The reported advantages of the NeuroSky MindSet include it being wireless, portable, relatively cheap (approximately £100), lightweight and non-invasive. Peters et al. (2009) believe that the NeuroSky MindSet provides “…the potential to conduct accurate user studies in more practical and naturalistic settings without inducing the stress or distractions of more elaborate scanning processes”. However, they also criticise the MindSet for providing a much coarser reading of brain activity compared to other multi-electrode EEG and BCI technologies. Guðmundsdóttir (2011) argues that the MindSet’s single point electrode is able to monitor a substantial part of the entire brain’s activity and that there is a stronger, steadier signal because there is no hair between the electrode and the scalp.

Grierson et al. (2011) report that NeuroSky devices do not “…appear to use P300 detection techniques, focusing instead on more established neurofeedback and sensory motor approaches.”.

Within the literature it is reported that EEG rhythms are often contaminated by artifacts, primarily due to facial muscle activity (Kohlmorgen et al., 2007) and biological signals such as those generated by eye movements (Hu et al., 2012). The Neurosky may be particularly susceptible to such artefacts due to the close proximity of the recording electrode to the eyes. However, Guðmundsdóttir (2011) reports that within every NeuroSky product there is a ThinkGear chip which amplifies the raw brainwave signal and removes ambient noise and artifacts.

3.5 NeuroSky research studies

3.5.1 Gaze tracking and NeuroSky

Peters et al. (2009) conducted a study which correlated the outputs of a gaze tracker and measured attention levels using NeuroSky to address the following questions:

• Are visually distracted users highly attentive cognitively?

• Do people fall into distinguishable patterns of attention?

The study emphasised the importance of real-time analysis of user behaviour and attention levels during agent-based interactions, stating that such analysis will help to understand the relationship between visual and cognitive attention. It was found that the NeuroSky would be most effective as a BCI in conjunction with one or more other modalities of detection, since the NeuroSky provides only partial information about attention.

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3.5.2 NeuroSky and Second Life

Rebolledo-Mendez et al., (2008) conducted a study which used inputs from NeuroSky technology and user-generated data from the computer game “Second Life” to determine whether the measured levels of attention correlated. Their approach did not correlate particular body postures or gaze activity to infer attention levels, but used the direct inputs associated with attention based on brain activity.

The method involved defining six types of reactions ranging from low levels of attention to sustaining high attention levels, and collecting measurements during the interaction considering episodes in which questions are posed. Each reading of attention was associated to a particular learning episode lasting more than one second. The episode may have consisted of solving a mathematical problem, answering a question or being exposed to reflective feedback. The data used to estimate levels of attention was related to performance (quality, speed and give-up), as outlined in Table 3. Keller’s ARCS model was used so that both low/high levels of attention could be detected and reaction feedback could be provided so that the learner could improve or sustain their attention.

Table 3 – Inputs associated to attention modelling

(Source: Rebolledo-Mendez et al., 2008)

To determine user-attention value, the following were considered:

• NeuroSky input (calculated as the mean value of all the attention inputs during the episode)

• Time taken to respond to question

• Correctness of question

• Whether the user gives-up or not.

Although there were no results provided in the paper, the study provided some promising indicators that the Neurosky headset could be an appropriate tool for use in educational research with computers.

3.5.3 NeuroSky and human emotional response

Crowley et al., 2010 conducted a study using NeuroSky’s MindSet to measure attention and meditation levels. Two psychological computer-based tests were used: a Stroop test (word-inference) and a test implementing the Towers of Hanoi. Both tests found that NeuroSky is able to monitor a subject’s level of meditation or stress over a given period of time, and the output clearly indicated when a subject undergoes a change in these emotions. However, precise moments of error were unable to be pinpointed and it is assumed that the stress response to making an error in the test is not instantaneous, i.e. the headset does not register the error at the precise moment the subject makes the mistake because the data output is not time-locked to specific events. Overall, the

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emotional patterns of the subjects were clearly visible using NeuroSky and the MindSet was considered to be suitable as a minimally invasive means of measuring the attention and meditation levels of a user.

3.5.4 NeuroSky and Meditation output

Guðmundsdóttir’s (2011) study used the Meditation eSense meter to investigate how behavioural suggestions (‘hints’) could be used to increase the output value of Meditation, and what impact these had on particular brainwave types. Various behaviours which are linked to increases in specific brainwaves were investigated. These included: closed eyes (Alpha Waves), a structured form of deep breathing (Alpha Waves), listening to non-harmonious calm music (Theta Waves) and focusing on a single object and continuously repeating a single word or mantra (Gamma Waves). The study found that it is not currently possible to build a system which gives directed hints to increase activity in certain wavebands in order to increase Meditation.

3.5.5 NeuroSky and Reading tasks

Mostow et al.’s 2011 study involved adults and children being asked to read text and isolated words out aloud and silently in order to find out whether EEG can detect when reading is difficult, whether EEG can detect lexical features and what EEG components are most sensitive. The study found that the NeuroSky device could discriminate between reading easy and hard sentences reliably and that it could detect frequency bands which were sensitive to difficulty. It also found that there were different predictors for adult and child oral sentence reading: the beta band for adults and the gamma band for children.

3.5.6 MIT Mind Controlled Cycle helmet

MIT has developed a cycle helmet (MindRider) which translates EEG feedback into an embedded LED display. For the user, green lights indicate a focused and active mental state, whilst red lights indicate drowsiness, anxiety, and other states not conducive to operating a bike or vehicle. Flashing red lights indicate extreme anxiety or panic. The MindRider is intended to support safety by adding visibility and increased awareness to the cyclist/motorist interaction process.

3.6 EEG and driving behaviour studies

Khalilardali et al.’s (2012) paper states that: “Previous studies of monitoring driver’s brain state have mainly focused on the level of driver’s drowsiness/arousal using EEG and EOG”.

3.6.1 Intention to brake

A study conducted by researchers at the Berlin Institute for Technology investigated how EEG correlates with intended emergency braking. A total of 18 subjects had electrodes attached to their scalps and were asked to keep 20 meters behind a simulated car which braked sharply at random intervals. EEG was used to analyse the drivers’ brain signals in order to detect the intention to brake. The study also analysed the muscle tension of the subjects to help determine which parts of the brain are key to braking.

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It was found that the intention to brake could be detected before any actions became observable (Haufe et al., 2011). The system was able to pinpoint the intention to brake 13 hundredths of a second before the driver applied pressure to the brakes, and it was found that the braking distance of a car travelling at 65mph could be reduced by 3.66 meters.

3.6.2 Predicting upcoming manoeuvres

Welke et al. (2009) were able to predict the upcoming action of a driver by an offline classification of variances in certain frequency bands of the EEG. In this study, the driver’s manoeuvres and their brain activity (recorded from either 64 or 32 EEG channels) were recorded and data from the car controller area network (CAN) was collected.

3.6.3 Anticipation

In a study by Khalilardali et al. (2012), anticipation (the cognitive state leading to specific actions during car driving) was investigated using EEG, EOG and EMG signal recordings from six subjects during simulated car driving. The results support the possibility of predicting drivers’ intentions and it is hoped that measuring driver’s intention from EEG could help to develop an in-car BCI system for intelligent cars.

3.6.4 Response to traffic lights

Ali’s 2012 study recorded EEG from three subjects using an EEG cap with 32 electrodes embedded in it. Participants were shown images of traffic light signals featuring one of the three signal states (red, amber, green) and it was found that EEG responses had no relationship with evoked responses obtained from the traffic light signals.

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4 Methodology

4.1 Aim and objectives

The aim of the study is to investigate how well the headset performs with regards to the quality and quantity of data it gathers in order to determine whether it could be more widely used as a cost-effective means of recording EEG output. The specific objectives for the study include:

• Objective 1: to investigate how useful the NeuroSky MindWave is in measuring attention levels.

• Objective 2: to investigate how useful the NeuroSky MindWave is in measuring relaxation levels.

• Objective 3: to understand the NeuroSky MindWave’s abilities and its limitations for future use.

The study seeks to determine whether the NeuroSky MindWave could be useful in providing basic, reliable and informative measures of a driver’s mental state.

4.2 Participants

A total of 24 participants (12 male and 12 female) aged between 21 and 65 (M = 38.3) took part in the study. Participants were given standard instructions before each test and told that whilst they should try hard to achieve the goals of each task, the purpose of the trial is not to evaluate their own performance.

4.3 Experimental design/procedure

The testing took place in the Simulator area at TRL and took approximately 45 minutes per participant. The testing procedure involved each participant undertaking six cognitive tasks, as outlined below:

• Task 1) Auditory Verbal Learning Test (AVLT): This involves recalling a list of unrelated words from memory. This test aims to measure the participant’s Gamma brainwaves (>30 Hz), which are associated with some senses and memory.

• Task 2) Digit Span Test: This involves recalling a series of digits in a given order. This test also aims to measure the participant’s Gamma brainwaves (>30 Hz), which are associated with some senses and memory.

• Task 3) Snowy Pictures Test (SPT): This involves identifying images from blurred or distorted pictures. This test aims to measure the participant’s Beta brainwaves (13-30 Hz), which are associated with the brain being aroused, mentally engaged, focused and attentive.

• Task 4) The Stroop Test: This is a word-inference test which requires the subject to name the colour that is displayed and not the word. This test was used in a similar study by Crowley et al. (2010) which evaluated the NeuroSky's MindSet headset (an earlier version of the MindWave) in measuring attention and meditation levels. This test also aims to Beta brainwaves.

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• Task 5) The Towers of Hanoi: This mathematical game / puzzle consists of three rods and a number of different sized disks which the subject has to move whilst obeying basic rules. This test was also used in the study by Crowley et al. (2010) which evaluated the NeuroSky's MindSet headset (an earlier version of the MindWave) in measuring attention and meditation levels. This test also aims to Beta brainwaves

• Task 6) Relaxation CD: This involves the subject sitting in a room, closing their eyes and listening to a relaxation CD. The test will aim to measure the participant’s meditation scores.

The six tasks were selected to measure the user’s attention and relaxation levels. The output from the Headset was exported into an Excel file and the results sought to determine whether the Headset is able to provide useful and/or reliable data. Further information on each of the six tasks is provided in Appendix B.

On completion of each task, the participant was asked to answer a self-completion workload assessment (see Appendix C) which measures self-report workload. The results from these were correlated with the data from the headset.

Between each test, participants had approximately one to two minutes ‘rest’ period whilst the experimenter prepared the next task.

Following the testing, the participant was asked to complete a short post-trial questionnaire (see Appendix D) which includes some demographic questions to enable basic information to be collected about the participant, along with some user opinions to understand how the subject felt about using the headset. The measurements used to record stress and workload levels therefore involved both physiological (raw output from the Headset) and subjective (self-report workload and post-trial questionnaire) measures. The results of the testing are discussed in Chapters 4 and 5.

4.4 Hypotheses

4.4.1 Hypotheses relating to attention and memory tests

The hypotheses to be tested include:

H01 The AVLT test will have no effect on the participant’s Gamma Brain Waves.

H02 The Digit Span test will have no effect on the participant’s Gamma Brain Waves.

H03 The Snowy Pictures Test (SPT) will have no effect on the participant’s Beta Brain Waves.

H04 The Stroop Test will have no effect on the participant’s Beta Brain Waves.

H05 The Towers of Hanoi test will have no effect on the participant’s Beta Brain Waves.

4.4.2 Hypotheses relating to Relaxation CD

H06 The Relaxation CD will have no effect on the meditation levels of the participant.

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4.4.3 Hypotheses relating to self-completion questionnaire

H07 There will be no differences between the relative levels of physiological and self-report workload scores.

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5 Results

5.1 Physiological data

Each of the hypotheses were tested and the results are presented below. The hypotheses were confined to the effects of task performance on Gamma and Beta frequencies, however, other frequencies and measures were reported by the MindWave software and these were also explored for useful patterns.

Data was processed and recorded by the MindWave device at 1Hz. This was lower than would typically be used by TRL projects, however, it was not possible to increase this frequency. The raw data appeared to be at much higher frequencies, however, the challenge of developing a technique for processing this raw signal was beyond the scope of this reinvestment project.

There were some issues with the data obtained from the MindWave; namely the device does not output data using standard power units (typically Volts-squared per Hz, or V2/Hz). This limits comparing its data with other EEG acquisition systems, such as the Neuroscan E-series machine used previously at TRL. NeuroSky states that the data undergoes “…a number of complicated transformations…” therefore, it is not possible to relate them to volts. Instead NeuroSky state that the units should be described as ASCI EEG Power Units. For the following figures, where the Y axis is labelled “power units” read “ASCI EEG Power Units”.

Furthermore, the data is split into a number of bands, including sub-divisions within the standard spectra (for example, Gamma is split into L Gamma and M Gamma, presumably “low” and “medium” respectively). For some of the analyses it would have been preferable to combine the sub-divisions, however, due to the non-standard units used this was not possible. Therefore, for hypotheses 1 to 5 two EEG band frequencies were analysed.

The EEG band frequencies are shown in Table 1:

Table 1 – EEG band frequencies

Band Frequency

Delta 1-3Hz

Theta 4-7Hz

L Alpha 8-9Hz

H Alpha 10-12Hz

L Beta 13-17Hz

H Beta 18-30Hz

L Gamma 31-40Hz

M Gamma 41-50Hz

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5.2 Physiological data

5.2.1 Distribution of the physiological data

Initially, the distribution of the data was examined to ensure parametric or non-parametric statistical tests were applied, as appropriate.

A Shapiro-Wilk test was performed on the data recorded whilst participants were completing a task, and whilst they were at rest between tasks (referred to as task and rest data, respectively). These tests showed that for nearly all measures the data was not normally distributed, therefore, in all analyses non-parametric statistics were applied.

For all paired comparisons between two measures, a Wilcoxon Signed Ranks test was performed.

Outliers were removed from the data. An outlier was defined as the top and bottom 5% of data points.

5.2.2 Gamma (Hypothesis 1)

Participants mean L Gamma and H Gamma activity when completing the tasks and during the associated rest period were compared. As can be seen in Figure 1, there was little difference between task and rest data for the Auditory Verbal Learning Task (AVLT) task, however, there was a difference for the Digit Span task.

Figure 1 – Mean L Gamma brainwaves (left); Mean M Gamma brainwaves

(right)

H01 The AVLT test will have no effect on the participant’s Gamma Brain Waves.

There was no significant difference between participants’ L Gamma activity when completing the AVLT task and when at rest after the task (z = -.74, p = .46).

There was also no significant difference when comparing M Gamma activity (z = -.51, p = .60).

H02 The Digit Span test will have no effect on the participant’s Gamma Brain Waves.

The difference in L Gamma activity when completing the Digit Span task and during the rest period was statistically significant (z= -2.89, p = <.01). The difference for M Gamma was also statistically significant (z = -2.94, p = < .01). Gamma power was

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reduced in the digit span task compared with rest period for both Gamma L and Gamma M.

Summary: The AVLT task did not seem to affect L Gamma or M Gamma activity, however, the Digit Span task did (it produced lower power when engaged in the task). These tests were chosen as they require the use of memory and senses, and it was therefore expected that Gamma power would be higher than rest, not lower, when engaged in any of these tests. The primary difference between the test was that AVLT requires participants to recall a list of words in any order, whereas the Digit Span requires remembering a list of numbers and then recalling them in the opposite order from which they were heard. It is possible this aspect of the Digit Span task contributed to the difference observed between the tasks, although it remains unclear why the direction of this difference was opposite to what was expected.

5.2.3 Beta

Differences in mean L Beta and H Beta activity when completing one of three tests or when at rest were compared: Snow-pictures Test (SPT), Stroop test (Stroop) and Towers of Hanoi (Towers). These means are displayed in Figure 2. The figures suggest that the Towers and Stroop produced differences between task and rest for both Beta frequencies. The differences for SPT were much smaller.

Figure 2 – Mean L Beta brainwaves (left); Mean H Beta brainwaves (right)

H03 The Snowy Pictures Test (SPT) will have no effect on the participant’s Beta Brain Waves.

When completing the SPT, participants’ L Beta and H Beta activity was not significant different between under task conditions and when we rest (L Beta z = -.29, p < .78; H Beta z = -1.03, p < .30).

H04 The Stroop Test will have no effect on the participant’s Beta Brain Waves.

The Stroop Test did not produce significant changes in either L Beta or H Beta (L Beta, z = -.29, p = .78; H Beta z = -.57, p = .59).

H05 The Towers of Hanoi test will have no effect on the participant’s Beta Brain Waves.

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The Towers of Hanoi test did produce significant changes in L Beta and H Beta (L Beta, z = -3.60, p < .01; H Beta z = -3.13, p < .01). Both types of Beta were lower when engaged with the task than when at rest.

Summary: Of the three tests used to produce active engagement and problem solving in participants only the Towers of Hanoi test produced significantly lower activity compared with its rest period. Why this test would have led to lower activity when the other two did not, is unclear.

5.2.4 Meditation

The MindWave device outputs a “meditation” measure. It is unclear how this measure is calculated (most likely though use of the Alpha band), however, its value as an indicator of a user’s vigilance/arousal level was tested. This was achieved by comparing mean meditation scores when completing the relaxation task against the meditation scores when engaged in the rest periods between all other tasks (a mean of all rest periods was calculated). As can be seen in Figure 3, meditation levels were higher (possibly an indication the participants were more relaxed) when engaged with the relaxation task than during the other rest periods.

Figure 3 – Mean Meditation brainwaves

H06 The Relaxation CD will have no effect on the meditation levels of the participant.

Whilst relaxing, participants showed significantly higher meditation levels than during all of the other rest periods combined (z = -3.14, p < .01).

5.2.5 Attention

Whilst the MindWave device does not report a single cognitive workload measure, it does report “attention”. Like the meditation measure, this is a synthetic measure and how it is calculated is not clear. However, for the purposes of evaluating the Mindwave device it was tested as a means of judging workload.

After each task participants completed the NASA-TLX subjective workload scale. These data were then correlated against attention scores gathered during each task. Figure 4 shows overall NASA-TLX workload scores plotted against attention scores.

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Figure 4 – TLX subjective workload scores against attention brainwaves for

each task

H07 There will be no differences between the relative levels of physiological and self-report workload scores.

Correlations of NASA-TLX scores and attention activity did not reveal any significant relationships between these two variables for any of the six tests undertaken by participants. Complete results can be seen in Table 2.

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Table 2 – NASA TLX and attention activity correlations for all tasks

TLX AVLT

TLX Digit Span

TLX SPT

TLX Stroop

TLX Towers

TLX Relaxation

r .367 .300 .159 -.338 .179 .162 Sig. .085 .155 .458 .124 .402 .461 N 23 24 24 22 24 23

5.3 Questionnaire results

Drivers were asked how comfortable the device was and how those feelings of comfort had changed by the end of the trial. They were also asked how aware they were of wearing the headset during the trial.

5.3.1 Initial comfort level

Participants were asked to rate how comfortable they found the device when they first put it on. A five point scale was used with the descriptive anchors of: very comfortable; comfortable; neither comfortable nor uncomfortable; uncomfortable; and very uncomfortable. The frequency of responses shown in Figure 5 demonstrates that the initial comfort level was mixed, with many participants finding the device uncomfortable (7 uncomfortable; 4 very uncomfortable), whilst slightly over half of the participants reported a neutral response or found it comfortable (10 neither comfortable nor uncomfortable; 3 comfortable).

Figure 5 – Initial comfort levels

5.3.2 Change in comfort level

Participants were also asked to rate how their comfort level changed by the end of the trial. Participants could select: much more comfortable; slightly more comfortable; no difference; slightly more comfortable; much more comfortable. The frequency of responses in each category can be seen in Figure 6. This figure shows opinions varied as to how more or less comfortable the device became, with few participants reporting a

0 3 10

7 4

0

2

4

6

8

10

12

Verycomfortable

Comfortable Neithercomfortable

noruncomfortable

Uncomfortable Veryuncomfortable

Freq

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large change in comfort levels. This suggests that for most participants wearing the device for 20 to 30 minutes only had modest, if any, effects on comfort levels.

Figure 6 – Change in comfort levels

5.3.3 Awareness of wearing device

In order to understand how likely a participant would be to be aware of wearing the device during trials, participants were asked to report how frequently they became aware of wearing it. Participants could select: All the time; most of the time; sometimes; rarely; not at all. As can be seen in Figure 7, the most common response was most of the time (9) and 5 participants were constantly aware of the device.

Figure 7 –Awareness of wearing device

1 5 10

7 1

0

2

4

6

8

10

12

Much morecomfortable

Slightly morecomfortable

No difference Slightly moreuncomfortable

Much moreuncomfortable

Freq

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5 9 6 4 0

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All the time Most of thetime

Sometimes Rarely Not at all

Freq

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6 Discussion Overall, our data produced mixed results. Table 3 displays the brain wave frequencies tested, associated tasks, whether we were able to reject the null hypothesis and the direction of the difference between the task and rest.

Table 3 – Results of hypotheses testing

H num.

Frequency tested

Task completed

Accept or reject null hypothesis?

Was power increased or reduced by task?

1 Gamma AVLT Accept No change

2 Gamma Digit span Reject Reduced

3 Beta Snow pictures Accept No change

4 Beta Stroop Accept No change

5 Beta Towers of Hanoi Reject Reduced

6 Meditation Relaxation Reject Increased

7 Attention NASA TLX Accept n/a

Only three of the null hypotheses were rejected, and of those three, two produced results in the opposite direction to that which was expected. Specifically the Digit Span test (which is a test of memory) produced lower Gamma power than when at rest, which was surprising given that Gamma is associated with memory; also, the Towers of Hanoi test produced lower Beta power then when at rest, which was also surprising given that Beta is associated with problem solving. Only the Relaxation measure produced changes in brain wave power which were consistent with our expectations (when participants relaxed their meditation power increased).

Explaining the results obtained is challenging. We have considered several possibilities to explain the data collected:

• Errors within the data – issues such as loss of signal and how the raw data is processed by the MindWave device may have decreased the reliability of the data.

• Sampling interval – the data is recorded at 1Hz, a frequency which is unusually low for TRL research purposes. It is possible that some of the variation within the data was ‘washed out’ by this.

• Task administered – the tasks selected for use in the trial were based on related research and expert judgement regarding the types of cognitive demands they placed on participants. However, we cannot discount the possibility that some aspects of the tests led to the powers observed. For example, we administered the Towers of Hanoi on a PC; it is possible that the electronic form of the test differs places different demands on a participant that the manual version (e.g. mouse operation, unintuitive user interface, etc.).

• Rest periods – In order to minimise trial duration (important as TRL staff members were volunteering their time) a brief rest period of one to two minutes was selected as a good compromise between data clarity and trial duration. It is

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possible that a longer rest period would have led to lower observed power, increasing the likelihood of finding significant differences between tasks and rest periods.

• Several aspects of the design of the MindWave design may have led to issues with signal quality:

o The recording electrode is located at position FP1 which is vulnerable to ocular related artefacts (eye blinks). Eye blinks generate much larger amplitudes and can mask the underlying EEG signal unless they are filtered out.

o The skin on the forehead (where the recording electrode was placed) tends to have higher electrical impedance compared with the scalp (because the skin is tougher). This can make it difficult to achieve a clean signal.

o The above factors could have meant that we had quite a poor signal-to-noise ratio.

6.1 Usefulness of MindWave to future work

From the data gathered the usefulness of the MindWave device appears to be limited.

Due to the low sampling frequency, it is not likely to be useful as a means of measuring ‘spot’ changes to brain wave activity in responses to sudden (e.g. reaction time stimuli) events.

The data gathered on other brain waves did not correspond to our expectations, limiting its usefulness as a means of measuring those frequencies. Further research may clarify its accuracy, but from the data gathered this project could not recommend its use as a low-cost, unobtrusive EEG.

The meditation measure did produce a significant result, which could be of use in future research which exposes participant to some form of fatigue. This could be of particular use. This project would recommend using the MindWave device during the next simulator trial to capture further meditation data in the expectation that it may add value as cross-validation of any self-report fatigue measures administered.

6.2 Future work

With modest additional budget two main questions could be addressed which could clarify the issues with the data observed.

Firstly, it would be advisable to compare task power data with the rest period power as it is possible that participants either did not have enough time to return to a neutral physiological state between tests, or they were experiencing task anxiety, inflating the power of their brain wave activity between tests. Follow-on work would seek to see if this comparison would be valid and whether it would reveal expected differences.

Secondly, direct contact with NeuroSky could be established to discuss the unusual nature of our data and to gain their advice as to why it might be the case. Furthermore, information on how to process the raw signal would be sought in order to more accurately eliminate noise, filter extreme values, and obtain data at higher sampling frequencies.

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Addressing these two options would help us to interrogate the data we have received in more depth and potentially reveal more fully the extent to which the MindWave could be used in future work at TRL. The promise of an unobtrusive, inexpensive EEG is enticing, and whilst the results of this project do not generally support the use of MindWave in this capacity, its capabilities are intriguing and if there was a method of obtaining more meaningful outputs, it could offer excellent added value to future research.

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Appendix A - References

Ali, Md Z. (2012) EEG-Based Assessment of Driver’s Cognitive Response In Virtual Traffic Light Environment, The Faculty of the College of Graduate Studies, Lamar University

American Electroencephalographic Society. (1994). Guidelines for standard electrode position nomenclature. Journal of clinical neurophysiology, 11, pp.111-113.

Cester, I., Riera, A., Whitmer, D., Dunne, S., Soria-Frisch, A. and Allison, B. (2011) Report on Sensors, Signals and Signal Processing, Future Directions in Brain/Neuronal Computer Interaction (Future BNCI)

Crowley, K., Sliney, A., Pitt, I. and Murphy, D. (2010) Evaluating a Brain-Computer Interface to Categorise Human Emotional Response, 2010 10th IEEE International Conference on Advanced Learning Technologies

Grierson, M. and Kiefer, C. (2011) Better Brain Interfacing for the Masses: Progress in Event-Related Potential Detection using Commercial Brain Computer Interfaces, CHI 2011, May 7–12, 2011, Vancouver, BC, Canada

Guðmundsdóttir, K. (2011) Improving players’ control over the NeuroSky Brain-Computer Interface, School of Computer Science, Reykjavik University

Haufe, S., Treder, M.S., Gugler, M.F., Sagebaum, M., Curio, G. and Blankertz, B. (2011) EEG potentials predict upcoming emergency brakings during simulated driving, Journal of Neural Engineering, 8, 056001

Hu, S., Zheng, G. and Peters, B. (2012) Driver fatigue detection from electroencephalogram spectrum after electrooculography artefact removal, IET Intelligent Transport Systems, Vol.7, Issue 1, pp.105-113

Khalilardali, Z., Chavarriaga, R., Gheorghe, L.A. and Millan, J.d.R. (2012) Detection of Anticipatory Brain Potentials during Car Driving

Kim, J.Y., Park, J.B. and Chung, B.J. (2001) Evaluation of Driver’s psychophysiological response in general driving using probability density function, Proceedings World Conference on Transport Research

Kohlmorgen, J., Dornhege, G., Braun, M.L., Blankertz, B., Muller, K-R., Curio, G., Hagemann, K., Bruns, A., Schrauf, M. and Kincses, W.E. (2007) Improving Human Performance in a Real Operating Environment through Real-Time Mental Workload Detection, MIT Press

Lei, S., Welke, S. and Roetting, M. (2009) Driver’s Mental Workload Assessment Using EEG Data in a Dual Task Paradigm, National Highway Traffic Safety Administration

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Miller, S., 2001 Literature Review – Workload Measures, The University of Iowa

Min, W. and Luo, G. (2009) Medical Applications of EEG Wave Classification, CHANCE, Vol.22, Issue 4, pp.14-20

Mostow, J., Chang, K. and Nelson, J. (2011) Toward Exploiting EEG Input in a Reading Tutor, AIED'11 Proceedings of the 15th international conference on Artificial intelligence in education

NeuroSky Inc. (2010) MindWave Sales Brochure,

http://www.mindtecstore.com/index.php/en/downloads/viewcategory/8-mindwave, accessed 26/07/13

Nijboer, F., Allison, B.Z., Dunne, S., Plass-Oude Bos, D., Nijholt, A. and Haselager, P. (2011) A preliminary survey on the perception of marketability of Brain-Computer Interfaces (BCI) and initial development of a repository of BCI companies, 5th International BCI Conference, September 2011

Peters, C., Asteriadis, S. and Rebolledo-Mendez, G. (2009) 10th International Workshop on Image Analysis for Multimedia Interactive Services, Institute of Electrical and Electronics Engineers, Inc.

Rebolledo-Mendez, G. and de Freitas, S. (2008) Attention modeling using inputs from a Brain Computer Interface and user-generated data in Second Life, Paper presented at The Tenth International Conference on Multimodal Interfaces (ICMI)

Stamps, K. and Hamam, Y. (2010), Towards Inexpensive BCI Control for Wheelchair Navigation in the Enabled Environment – A Hardware Survey, LNAI 6334, pp. 336–345, Springer-Verlag Berlin Heidelberg 2010

Teplan, M. (2002) Fundamentals of EEG Measurement, Measurement Science Review, Volume 2, Section 2

Watling, C.N., Smith, S.S. (2012) Too sleepy to drive: self-perception and regulation of driving when sleepy, Centre for Accident Research and Road Safety - Queensland (CARRS-Q), Queensland University of Technology

Welke, S., Jurgensohn, T. and Roetting, M. (2009) Single-Trial Detection of Cognitive Processes for Increasing Traffic Safety, National Highway Traffic Safety Administration

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http://www.mindtecstore.com/index.php/en/downloads/viewcategory/8-mindwave


Appendix B – Tests used in the study

TEST 1: REY AUDITORY VERBAL LEARNING TEST (AVLT) 1 Trial I (List A)

Say, I am going to read a list of words. Listen carefully, for when I stop you are to say back as many words a you can remember. It doesn't matter in what order you repeat them. Just try to remember a many as you can. Record correct responses.

Trial II (List A)

Say, Now I'm going to read the same list again, and once again when I stop I want you to tell me as many words as you can remember, including words you said the first time. It doesn't matter in what order you say them. Just say as many words as you can remember, whether or not you said them before. Record correct responses.

Trial III (List A)

Say, Now I'm going to read the same list again, and once again when I stop I want you to tell me as many words as you can remember, including words you said the first time. It doesn't matter in what order you say them. Just say as many words as you can remember, whether or not you said them before. Record correct responses.

Score = total words correctly recalled across all 3 trials

Stimuli (to be read out at the rate of one every 2 seconds)

AVLT1_I AVLT1_II AVLT1_III

Drum

Curtain

Bell

Coffee

School

Parent

Moon

Garden

Hat

Farmer

Nose

Turkey

Color

House

River

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TEST 2: WAIS-R DIGIT SPAN

FWD: Start with item 1. Say “I am going to say some numbers. Listen carefully, and when I have finished say them right after me”. The digits should be given at the rate of one per second. Let the pitch of your voice drop on the last digit of each trial. Administer both trials of each item, even if the ppant passes trial 1. DISCONTINUE after failure on both trials of any item.

BWD: Start with item 1. Say “Now I am going to say some more numbers, but this time when I stop I want you to say them backwards. For example, if I say 7-1-9, what would you say?” Pause for the ppant to respond. If the ppant responds correctly (9-1-7) say “That’s right”, and proceed to item 1. As with Digits Forward, read the diguts at the rate of one per second and administer both trials of each item, even if the ppant passes trial 1. However, if the ppant fails the example, say, “No, you would say 9-1-7. I said 7-1-9, so to say it backwards you would say 9-1-7. Now try these numbers. Remember, you are to say them backwards. 3-4-8.” Whether the ppant succeeds or fails with the second example (8-4-3), proceed to item 1. Give no help on this second example or on any of the items that follow. DISCONTINUE after failure on both trials of any item.

DIGITS FORWARD Pass-Fail

Score 2,1, or 0

DIGITS BACKWARD Pass-Fail

Score 2,1, or 0

1 5-8-2 1 2-4 6-9-4 5-8

2 6-4-3-9 2 6-2-9 7-2-8-6 4-1-5

3 4-2-7-3-1 3 3-2-7-9 7-5-8-3-6 4-9-6-8

4 6-1-9-4-7-3 4 1-5-2-8-6 3-9-2-4-8-7 6-1-8-4-3

5 5-9-1-7-4-2-8 5 5-3-9-4-1-8 4-1-7-9-3-8-6 7-2-4-8-5-6

6 5-8-1-9-2-6-4-7 6 8-1-2-9-3-6-5 3-8-2-9-5-1-7-4 4-7-3-9-1-2-8

7 2-7-5-8-6-2-5-8-4 7 9-4-3-7-6-2-5-8 7-1-3-9-4-2-5-6-8 7-2-8-1-9-6-5-3

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TEST 3: SNOWY PICTURES

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Snowy Pictures – Answers

List 1

1) Sailboat 2) Tent 3) Dog 4) Chair 5) Iron 6) Flashlight 7) Fly/bug/bee

List 2

1) Lamp 2) Hand/glove 3) Table/coffee table/bench 4) Bridge 5) Bird 6) Umbrella 7) Monkey (in tree)

List 3

1) Horse 2) Knife 3) Fish 4) Guitar 5) Duck 6) Scissors 7) Planet (Saturn) satellite

(Left out eye (example), typewriter and football)

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TEST 4: THE STROOP TEST

See: http://psych.hanover.edu/JavaTest/CLE/Cognition/Cognition/Stroop.html

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http://psych.hanover.edu/JavaTest/CLE/Cognition/Cognition/Stroop.html


TEST 5: THE TOWERS OF HANOI

See: http://www.softschools.com/games/logic_games/tower_of_hanoi/

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http://www.softschools.com/games/logic_games/tower_of_hanoi/


Appendix C - Self-completion questionnaire To be completed by TRL

Participant Number: ____________ Drive number: _________ Date: _____/_____/_____

NeuroSky MindWave Study, November 2013 - NASA TLX

Section A. Your experience of the test NASA TLX

For the following questions please think about the test you have just undertaken and place an “X” along each scale at the point that best indicates your experience.

Some of the scales may seem strange at first glance. If you’re not confident that you have understood the descriptions of the scales, please do not hesitate to ask an experimenter for further clarification.

A1 Mental Demand: How much mental and perceptual activity was required (e.g., thinking, deciding, calculating, remembering, etc.)? Was the test easy or demanding, simple or complex?

Low High

A2 Temporal demand: How much time pressure did you feel due to the rate or pace at which the test took place? Was the pace leisurely or rapid and frantic?

Low High A3 Performance: How successful do you think you were in accomplishing the goals of the test? How

satisfied were you with your performance in accomplishing these goals?

Low High A4 Effort: How hard did you have to work (mentally) to accomplish your level of performance?

Low High A5 Frustration: How discouraged, stressed, irritated, and annoyed verses gratified, relaxed, contented, and

complacent did you feel during your test?

Low High

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Appendix D - Post-trial questionnaire To be completed by TRL

Participant Number: ____________ Time slot: _________ Date: _____/_____/_____

NeuroSky MindWave Study, November 2013

SECTION B. Background information B1. What was your age at your last birthday?

B2. Are you Male or Female? (please tick) Male Female

SECTION C. User Opinions In this section of the questionnaire you will be asked a range of questions about the headset you were wearing. There are no right or wrong answers. We are interested in your opinions so please do not worry about your answers, nor deliberate over them for too long. Your first impressions are of most interest to us.

C1. How initially comfortable did you find wearing the headset?

Very comfortable Comfortable

Neither comfortable nor uncomfortable

Uncomfortable

Very uncomfortable

C2. How did the comfort of the headset change with time?

Much more comfortable

Slightly more comfortable

No difference Slightly more uncomfortable

Much more uncomfortable

C3. Were you always conscious of wearing the headset?

All the time Most of the time Sometimes Rarely Not at all

C4. Do you have any safety concerns regarding the headset? (please state)

C5. Do you have any other comments regarding the headset? (please state)

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