Multiclass Classification of Word Imagination Speech With Hybrid Connectivity Features

Objective: In this study, electroencephalography data of imagined words were classified using four different feature extraction approaches. Eight subjects were recruited for the recording of imagination with five different words, namely; “go,” “back,” “left,” “right,” and “stop.” Methods: One hundred trials for each word were recorded for both imagination and perception, although this study utilized only imagination data. Two different connectivity methods were applied, namely; a covariance-based and a maximum linear cross-correlation-based connectivity measure. These connectivity measures were further computed to extract the phase-only data as an additional method of feature extraction. In addition, four different channel selections were used. The final connectivity matrix from each of the four methods was vectorized and used as the feature vector for the classifier. To classify EEG data, a sigmoid activation function-based linear extreme learning machine was used. Result and Significance: We achieved a maximum classification rate of 40.30% (p <; 0.007) and 87.90% (p <; 0.003) in multiclass (five classes) and binary settings, respectively. Thus, our results suggested that EEG responses to imagined speech could be successfully classified using an extreme learning machine. Conclusion: This study involving the classification of imagined words can be a milestone contribution toward the development of practical brain-computer interface systems using silent speech.