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Brain Computer Interfaces: Digital Signal Processing of Steady-State Visually Evoked
Potentials
Ian Linsmeier & Ahmed SaifECE630
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Brain Computer Interface (BCI)
Vialatte et al. Prog Neurobiol. 2010, 90(4).
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Dependent vs Independent BCIs
• Dependent BCI – System is dependent upon a minimal level of neuromuscular control by the user
• Independent BCI – System is independent of neuromuscular control by the user (not necessary)
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Steady State Visually Evoked Potential-Brain Computer Interface
(SSVEP-BCI) System Overview
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Repetitive Visual Stimulus (RVS)
Vialatte et al. Prog Neurobiol. 2010, 90(4).
Flickering LED(Simple Flicker)
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Steady State Visually Evoked Potential (SSVEP)
Vialatte et al. Prog Neurobiol. 2010, 90(4).
RVS frequency→ 10HzSSVEP → 10Hz
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SSVEP-BCI System Components
Vialatte et al. Prog Neurobiol. 2010, 90(4).
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Designing a SSVEP-BCI System
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SSVEP-BCI Design Parameters
1. Repetitive Visual Stimuli 2. Brain Signal Measurement3. SSVEP Detection4. SSVEP Classification
Vialatte et al. Prog Neurobiol. 2010, 90(4).
1
23 & 4
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RVS Design
1 RVS = 1 User Option
• Number of RVS’s• Simple vs. Complex• Frequency Range – 3.5 to 75 Hz– 15 Hz is optimal
Vialatte et al. Prog Neurobiol. 2010, 90(4).
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Measuring SSVEP
Itai et al. EMBC Annual International Conference. 2012.
• Measurement Location– Visual Cortex
• Number of electrodes – 1 or 2 is usually sufficient
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Two General BCI Paradigms
1. Small number of user options (≤4) Usually employ Complex RVS’s due to higher SNR
2. Large number of user options (>4) Usually employ simple RVS’s
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SSVEP Detection Methods
• Power Spectral Density (PSD) Analysis– Nonparameteric Methods (Fourier Analysis)– Parametric Methods (AR Modeling)
• Canonical Correlation Analysis (CCA)• Continuous Wavelet Transform (CWT)
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Nonparametric PSD Analysis
𝑆𝑥𝑥 (𝜔 )= ∑𝑘=−∞
∞
𝑟𝑥𝑥 [𝑘 ]𝑒− 𝑗𝑘𝜔
Bin et al. J. Neural Eng. 2009, 6(4).
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Periodogram Estimates PSD
(Asymptotically Unbiased as L → ∞)
(Not a consistent estimator)
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Averaged Periodogram
Break down signal into intervals of fixed length and average each interval together
No Averaging → 10 Interval Average → 20 Interval Average
Vialatte et al. Prog Neurobiol. 2010, 90(4).
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Parametric PSD Analysis
Parametric Models:– Moving Average (MA) – All Zeros– Autoregressive (AR) – All Pole– Autoregressive Moving Average (ARMA) – Poles and Zeros
Smondrk et al. IEEE. 2013.
𝑤 [𝑛 ]𝑥 [𝑛 ]
𝐻 (𝑒 𝑗 𝜔 )𝑆𝑥𝑥 (𝜔 )=𝑆𝑤𝑤 (𝜔 )|𝐻 (𝑒 𝑗𝜔 )|2
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AR Modeling of SSVEP Signals
∑𝑘=0
𝑁
𝑎𝑘𝑥 [𝑛−𝑘 ]=𝑤 [𝑛 ] ;𝑎𝑜=1
Caclulate ak coefficients using the Yule Walker Equations:
http://paulbourke.net/miscellaneous/ar/
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Canonical Correlation Analysis (CCA)
Lin et al. IEEE Trans. Biomed. Eng. 2007, 54(6)
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Continuous Wavelet Transform (CWT)
• Wavelets can localize a signal in both frequency and time
• Acts like a short time Fourier transformation but with varying window sizes based on frequency
• With the correct mother wavelet we can achieve a result better than the FFT and PSD
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SSVEP Classification
Yeh et al. Biomed Eng Online. 2013, 12(46)
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Support Vector Machine (SVM)
http://en.wikipedia.org/wiki/File:Svm_separating_hyperplanes_(SVG).svg
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A Comparison of SSVEP Detection Methods
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Comparison of SSVEP Detection Methods
Method The average time of calculation [ms]
PSD 1.8 ± 0.1PSDw 1.1 ± 0.1
AR 13.7 ± 0.6ARw 10.2 ± 0.4CCA 52.6 ± 0.7CWT 114.2 ± 2.8
Smondrk et al. IEEE. 2013.
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Comparison of SSVEP Detection Methods
Smondrk et al. IEEE. 2013.
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SSVEP Detection for BCI Paradigms
Paradigm 1: Systems will small number of user options (≤4 options) – Employ Complex RVS’s (checkerboard) – Nonparametric PSD using well resolved RVS’s
Paradigm 2: Systems using large number of user options (>4 options)– Employ Simple RVS’s (LEDs)– Canonical Correlation Analysis
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Questions?