Electroencephalography Signal Analysis for Human Activities Classification: A Solution Based on Machine Learning and Motor Imagery

Abstract

Electroencephalography (EEG) is a fundamental tool for understanding the brain’s electrical activity related to human motor activities. Brain-Computer Interface (BCI) uses such electrical activity to develop assistive technologies, especially those directed at people with physical disabilities. However, extracting signal features and patterns is still complex, sometimes delegated to machine learning (ML) algorithms. Therefore, this work aims to develop a ML based on the Random Forest algorithm to classify EEG signals from subjects performing real and imagery motor activities. The interpretation and correct classification of EEG signals allow the development of tools controlled by cognitive processes. We evaluated our ML Random Forest algorithm using a consumer and a research-grade EEG system. Random Forest efficiently distinguishes imagery and real activities and defines the related body part, even with consumer-grade EEG. However, interpersonal variability of the EEG signals negatively affects the classification process.

State-of-the-Art on Brain-Computer Interface Technology

“…These devices typically employ Bluetooth connectivity so that they can transmit their data wirelessly directly to computers for analysis. Recent advancements have also led to improvements in signal processing algorithms, which enable better detection and analysis techniques [52][53][54]. For example, it is now possible to detect subtle changes within short periods of time, leading to improved diagnosis capabilities.…”