EEG Processing for Brain-Computer Interface using Wavelet-Decomposition Ant Colony Optimization and Machine Learning

The research in this paper leverages Electroencephalography (EEG) to develop a Brain-Computer Interface (BCI). By processing and analyzing EEG signals, the system translates brain activity into commands, enabling individuals with severe motor impairments to control devices and regain independence. The Berlin BCI Competition IV dataset is used in the study. EEG recordings of healthy subjects doing hand and foot motor imagery tasks are included in the collection. Discrete wavelet transformation (DWT) decomposes the EEG signals into sub-bands to obtain pertinent information. Feature selection is performed using the ant colony optimization algorithm (ACOA) to reduce dimensionality and improve classification accuracy. An evaluation of three robust classifiers-support vector machine (SVM), weighted k-nearest neighbors (Weighted k-NN), and wide neural networks (WNN)-is conducted to ascertain their capability in Motor Imagery (MI) task classification. The results demonstrate that the SVM classifier in conjunction with DWT and ACO, achieves the highest average accuracy score of 85.36% while achieving a dimension reduction of 2.064-times. It makes it an appealing approach for the BCI systems.