Spatial decoding of oscillatory neural activity for brain computer interfacing

Abstract

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 the first two fingers (thumb and index) vs. the others (middle, ring and little). In the remaining parts of the thesis, we investigate the problems of CSP method and propose techniques to overcome these problems. The CSP method generally overfits the data when the number of training trials is not sufficiently large and it is sensitive to daily variation of multichannel electrode placement, which limits its applicability for everyday use in BMI systems. The amount of channels used in projections should be limited to some adequate number to overcome these problems. We introduce a spatially sparse projection (SSP) method, taking advantage of the unconstrained minimization of a new objective function with approximated 1 penalty. Furthermore, we investigate the greedy 0 norm based channel selection algorithms and propose oscillating search (OS) method to reduce the number of channels. OS is a greedy search technique that uses backward elimination (BE), forward selection (FS) and recursive weight elimination (RWE) techniques to improve the classification accuracy and computational complexity of the algorithm in case of small amount of training data. Finally, we fuse the discriminative and the representative characteristic of the data using a baseline regularization to improve the classification accuracy of the spatial projection methods.