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We propose a Transfer learning-enabled electroencephalography-based intuitive brain-computer interface system by utilizing phase-based brain functional connectivity methods such as phase lag index (PLI) and Intersite phase clustering (ISPC) along with power features to explore both phase and power-based information from electroencephalography (EEG) signals. Time-frequency decomposition using a complex morlet wavelet is applied to analyze the signal components in both the time and frequency domains and extract phase connectivity and power features. Functional connectivity methods aim to recognize functional interactions and statistical mutuality among signals acquired across various brain areas. The phase-based connectivity features are extracted simultaneously for multiple channels to investigate the phase synchronization among EEG signals across the entire brain. Next, Graph theory is adopted to trace connectivity between brain regions by calculating the connectivity degree of extracted PLI and ISPC features with other electrodes. In Parallel, Discrete wavelet convolution is performed to calculate the time variable frequency band’s specific power from the imagined speech EEG data. Finally, Time-frequency images of the above-mentioned PLI, ISPC, and EEG power features are fed as input to DenseNet-121 architecture for classification. Dense Net architecture overcomes the problem of ‘vanishing gradient’ by connecting each layer directly with other layers, making the network densely connected. The maximum classification accuracy achieved is 100%, 99.14%, and 98.72% for binary, three-class, and four-class classifications, respectively. The experimental results indicate that the proposed phase-based connectivity features, EEG power, and the DenseNet-121 model have achieved excellent accuracy for two public datasets, outperforming the state-of-the-art methods. The outstanding results strengthen the possibility of real-time EEG-based intuitive brain-computer interface communication. |
