Spatial Decoding of Oscillatory Neural Activity for Brain Computer Interfacing

SPATIAL DECODING OF OSCILLATORY NEURAL ACTIVITY FOR BRAIN COMPUTER INTERFACING İbrahim Onaran PhD in Electrical and Electronics Engineering Supervisor: Prof. Dr. A. Enis Çetin June, 2013 Neuroprosthetics (NP) aim to restore communication between people with debilitating motor impairments and their environments. To provide such a communication channel, signal processing techniques converting neurophysiological signals into neuroprosthetic commands are required. In this thesis, we develop robust systems that use the electrocorticogram (ECoG) signals of individuated finger movements and electroencephalogram (EEG) signals of hand and foot movement imageries. We first develop a hybrid state detection algorithm for the estimation of baseline (resting) and movement states of the finger movements which can be used to trigger a free paced neuroprosthetic using the ECoG signals. The hybrid model is constructed by fusing a multiclass support vector machine (SVM) with a hidden Markov model (HMM), in which the internal hidden state observation probabilities are represented by the discriminative output of the SVM. We observe that the SVM based movement decoder improves accuracy for both large and small numbers of training dataset. Next, we tackle the problem of classifying multichannel ECoG related to individual finger movements for a brain machine interface (BMI). For this particular problem we use common spatial pattern (CSP) method which is a popular method in BMI applications, to extract features from the multichannel neural activity through a set of spatial projections. Since we try to classify more than two classes, our algorithm extends the binary CSP algorithm to multiclass problem by constructing a redundant set of spatial projections that are tuned for paired and group-wise discrimination of finger movements. The groupings are constructed by merging the data of adjacent fingers and contrasting them to the rest, such as v