Random forest classifies real and imagined motor EEG signals accurately
Electroencephalography Signal Analysis for Human Activities Classification: A Solution Based on Machine Learning and Motor Imagery
Networking and Internet Architecture
Summary
Understanding brain signals when people move or imagine moving can help develop tools controlled by thoughts. The authors used a machine learning method called Random Forest to identify whether brain signals come from actual movements or imagined ones and also which body part is involved. They tested their method on two types of EEG devices, including a consumer-level one, and it worked well. However, brain activity patterns vary between people, which makes classification harder.
What this means in practice
- •For assistive technology developers: Build devices controlled by thought that distinguish imagined and real movements using EEG signals from affordable consumer devices.$Commercial implications: This enables affordable thought-controlled assistive tools for people with physical disabilities, improving independence.
- •For wearable device designers: Incorporate EEG-based classification of motor imagery to create intuitive control schemes for body-related commands in wearable electronics.
Authors
Tarciana C de Brito Guerra, Taline Nóbrega, Edgard Morya, Allan de M. Martins, Vicente A de Sousa
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.