Intelligent Machine Learning Based EEG Signal Classification Model

In recent years, Brain-Computer Interface (BCI) system gained much popularity since it aims at establishing the communication between human brain and computer. BCI systems are applied in several research areas such as neuro-rehabilitation, robots, exoeskeletons, etc. Electroencephalography (EEG) is a technique commonly capturing signals. It incorporated has attractive features non-invasive nature, high time-resolution output, mobility cost-effective. EEG classification process highly essential decision making incorporates different processes namely, feature extraction, selection, classification. With this motivation, current paper presents an Intelligent Optimal Fuzzy Support Vector Machine-based EEC recognition (IOFSVM-EEG) model for system. Independent Component Analysis (ICA) onto proposed IOFSVM-EEG to remove artefacts that exist signal retain meaningful information. Besides, Common Spatial Pattern (CSP)-based extraction utilized derive helpful set of vectors from preprocessed Moreover, OFSVM method signals, which parameters involved FSVM optimally tuned using Grasshopper Optimization Algorithm (GOA). order validate enhanced outcomes model, extensive experiments was conducted. The were examined under distinct aspects. experimental results highlighted performance presented over other state-of-the-art methods.