Individual and Mutual Feature Processed ELM Model for EEG Signal Based Brain Activity Classification
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BCI deals to map the brain signal or activity to evaluate the human behaviour, activities or disease. The aim of this research is to utilize the different features of EEG signal to recognize the brain activity. The composite feature model with ELM classification method is presented in this research to recognize the human activity. In this paper, multiple aspects including time domain, frequency domain and least square evaluation based features are processed under ELM classifier to recognize the human-activities. Multiple quantified features are generated under each time, frequency and the least square categories. These features are processed individually and mutually with probabilistic evaluation to expand the processing-featureset. This expanded-composite featureset is trained under ELM (Extreme Learning Machine) classifier to perform intra-class and inter-class classification. The experimentation is applied on five distinctive experiments of Dataset IIIa of BCI completion III. Each experiment is conducted with variant training and testing instances. The evaluation results identified that the proposed hybrid model has achieved the average accuracy over 80%. Comparative results are generated against ANN, SVM, KNN and Multiscale Wavelet Kernel ELM by utilizing each kind of individual and mutual feature. The results taken from various experimentations have validated that the proposed model has improved the accuracy against each of the existing feature processed classification methods.
KeywordsEEG Brain activity classification BCI Thought processing
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