ANALYSIS AND CLASSIFICATION OF EEG SIGNALS USING MACHINE LEARNING ALGORITHMS

Abstract

Electroencephalography (EEG) data analysis and categorization are critical for recognising brain activity and diagnosis different neurological illnesses. This research describes a unique approach for analysing and classifying EEG data, as well as its possible uses for health care and brain-computer interface, or BCI, applications. To extract relevant characteristics from EEG data, the proposed method employs modern signal processing methods and machine learning algorithms. These properties record both of them temporal and spectral aspects of brain activity, allowing for good differentiation among various brain states and disorders. Pre-processing procedures are included in the approach to reduce noise and artefacts, assuring the dependability of the recovered features. The approach uses supervised learning methods that include SVM, ANN, and RF to identify EEG data. These algorithms use tagged EEG data to teach them to recognise distinct brain states or diagnose certain neurological diseases. Performance indicators such as specificity, sensitivity, and total accuracy are used to assess classification accuracy, proving the usefulness of the given strategy. This approach has several uses in both clinical and scientific settings. It can help with the diagnosis of neurological illnesses such as epilepsy, sleep disorders, and Alzheimer's disease in clinical settings. Healthcare providers may make educated judgements about patients’ treatment and management by appropriately identifying EEG signals. Furthermore, the technology has the potential to be used in the development of brain computer interfaces, which would allow people to control external equipment using their brain activity. This has consequences for persons with movement impairments who use assistive devices, neurorehabilitation, and communication systems. The method provided here takes a new approach to EEG data processing and categorization, offering essential insights into brain activity and aiding in the diagnosis of neurological diseases. Its applications include healthcare and BCI systems, which promote developments in the field of neuroscience, medicine, and technology for people with disabilities. To broaden its scope and utility, future study may look at further upgrades, connection with other methods, and real-time deployment.