Studies On Classfication Algorithms Of Motor Imagery-based Brain-computer Interfaces

The classification performance of a brain-computer interface (BCI) depends on the user's ability to control his/her brain state, data recording methods and classification algorithms. Studies on classification algorithms of motor imagery-based BCIs, based on two types of data recordings, electroencephalogram (EEG) and electrocorticogram (ECoG), are presented in this dissertation.Five subjects participated in an on-line BCI experiment during which they were asked to imagine either left or right hand movement. The EEG recordings from all subjects were analysized off-line. The three multichannel linear descriptors, global field strength, global frequency of field changing and spatial complexity, could characterize the overall state of the brain. The three measures were applied alone and together to three electrode subsets determined by neurophysiological a priori knowledge, and the resulting feature vectors were used for classifying the data from the five subjects. For the eight feature vectors derived from 7 and 11 electrodes, the best and averaged classification accuracies of five subjects range from 85% to 99.9% and from 89% to 93.5% respectively.Amplitude and phase coupling measures, quantified by nonlinear regressive (NLR) coefficients and phase locking values (PLV) respectively, provide a new route for BCI feature extraction. The two measures were separately appied to two coupling methods decided by neurophysiological a priori knowledge and a small number of electrodes of interest, the resulting 6 feature vectors were used for classifying the data from the five subjects. Results indicated that coupling measures have good separability for the motor imagery data, and the combination of coupling features and AR features could significantly improve classification accuracy.To promote the development and practicality of ECoG-based BCIs, the organisors of BCI Competition III provided an ECoG data of imagined movement of left small finger or tongue in which the training set and test set were recorded in two different sessions, and required the contributing algorithms to have the ability of session-to-session transfer. We presented an algorithm of feature combination for classifying the data set. Three feature vectors, based on movement-related potentials (MRP) and event-related desynchronization (ERD), were extracted by common spatial subspace decomposition and waveform mean, then they were reduced to one dimension by Fisher discriminant analysis and concatenated into a three-dimensional feature vector, and finally a linear support vector machine was used for classification. The algorithm posseses the characteristics of high classification accuracy, good robustness and strong generalization ability, and thus achieves the high classification accuracy of 91% on test set. The result ranks first among 27 contributions from the whole world.To simplify the complexity of the competition algorithm and increase its operating speed, a feature subset selection-based algorithm was presented for classifying the competition data. 10 optimal leads are chosen according to the averaged band power difference between the two classes of signals, then the association information among these leads is extracted by nonlinear regressional coefficients in two frequency bands 0-3Hz and 8-30Hz and concatenated into a 200-dimensional feature vectors. To eliminate redundant information and retain meaningful features for classification, an optimal subset of 29 features is picked out by combining a genetic algorithm for feature selection with a support vector machine for their evaluation. The algorithm achieves a classification accuracy of 87% on test set that is comparable with the competition algorithm, and has higher operating speed.