A Brain-Computer Interface (BCI) acquires brain signals, extracts informative features, and translates these features to commands to control an external device. This work investigates the application of a non-invasive Electroencephalography (EEG)-based BCI to identify brain signal features in regard to actual hand movement . This provides a more refined control for a BCI system in terms of movement parameters. An experiment was performed to collect EEG data from subjects while they performed right and left hand movement. The informative features from the data was obtained using the Wavelet-Common Spatial Pattern (W-CSP) algorithm that provided high temporal-spatial-spectral resolution. The applicability of these features to classify the two movement and to reconstruct the movement profile was studied. SVM classifier is used to classify the two class of hand movement. The spatial patterns of the W-CSP features obtained showed activations in parietal and motor areas of the brain. This work promises to provide a more refined control in BCI by including control of movement speed.
3. INTRODUCTION
Brain computer Interface
Patients with neuro-muscular disorders
Movement related features use brain signals
Presence of movement information in the very low frequency
bands of the EEG data
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4. BACKGROUND
MEG signal in the low frequency (2-5 Hz) found to contain movement-speed related data
Event Related Potential of Sensory Motor Rhythm (SMR) to analyze speed-related data
OBJECTIVES
To investigate the presence of movement-related parameters in lowband
To classify the speed of movement using LF components
To study how these are affected if the movement is performed in 4 different directions.
To develop a BCI with a more refined control
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1. Feature extraction
1.1 Common Spatial Pattern (CSP)
Optimally discriminate between two classes of EEG data
Z = WX (1) Where, W = CSP Projection matrix
X = EEG data in a single trial of size N x T,
N = the number of channels used
T = the number of samples recorded in each trial
Covariance matrix , C= 𝑿𝑿`
𝒕𝒓 𝑿𝑿 `
(𝟐)
FeatureVector , fp = (
𝑣𝑎𝑟 𝑍𝑝
𝑖=1
2𝑗
𝑣𝑎𝑟(𝑍𝑖)
) (3)
7. 1.2 Wavelet-CSP Algorithm
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Daubechies wavelets for the creating filter banks
Half band lowpass filter and half band highpass filter
Reconstructed from subspaces using reverse process
Each subband are filtered using CSP
W-CSP is obtained by 𝒛 𝒘
𝑳
= 𝒘 𝑳 𝒙 𝝎
𝑳
Distinctive feature obtained by feature vector
equation
Fig 1.2 :
(a) Signal decomposition and reconstruction using
filters and up/down sampling,
(b) Signal decomposition into subspaces to produce
similar results as in DWT at each level.
2. Fisher Linear Discriminant (FLD) Classifier
Maximizes the ratio of between class scatter to within
𝐹 =
𝐹′ܵB𝐹
𝐹′ܹܵ𝐹
where, SB = between class scatter matrix
Sw = within class scatter matrix obtained from the feature
8. 3. Experiment Protocol
Four direction – North, South, East, West
Slow Movement – 1200ms
Fast Movement – 400ms
Fig 1.3: direction and speed studied
8
9. 4.1 Comparisons
4. Result Analysis:
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Low Frequency < 7
High Frequency 7-100
All frequency 1-100
Low frequency performs
better
W-CSP has greater accuracy
Fig 1.4 : Comparison among various methods
10. 4.2 Effect of muscular activation
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Fig 1.5 : Better performance using cross validation
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Fig. 7. (i-v) Spatial patterns obtained at five lower frequency subbands
by W-CSP method for subject 1
E. Discriminating features as shown by CSP
Activation in contra
lateral motor
parietal cortex.
12. Conclusion
Low frequency EEG band related to movement information
Wavelet-CSP algorithm has classification accuracy of 83.71%
Spatial patterns showed the activation in contra lateral motor area and parietal
regions
Showed the possibility of introducing a refined control command set to BCI system
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