The evolution of AI approaches for motor imagery EEG-based BCIs

TL;DR

This paper reviews the evolution of AI techniques applied to motor imagery EEG-based BCIs, highlighting their development, influence, and application across different datasets and experimental paradigms.

Contribution

It provides a comprehensive survey of how AI approaches have evolved and impacted MI EEG-based BCIs over time and across various datasets.

Findings

AI techniques have significantly advanced MI EEG-based BCIs.

Public datasets have facilitated the testing of new AI methods.

The evolution of AI has improved BCI performance and usability.

Abstract

The Motor Imagery (MI) electroencephalography (EEG) based Brain Computer Interfaces (BCIs) allow the direct communication between humans and machines by exploiting the neural pathways connected to motor imagination. Therefore, these systems open the possibility of developing applications that could span from the medical field to the entertainment industry. In this context, Artificial Intelligence (AI) approaches become of fundamental importance especially when wanting to provide a correct and coherent feedback to BCI users. Moreover, publicly available datasets in the field of MI EEG-based BCIs have been widely exploited to test new techniques from the AI domain. In this work, AI approaches applied to datasets collected in different years and with different devices but with coherent experimental paradigms are investigated with the aim of providing a concise yet sufficiently comprehensive survey on the evolution and influence of AI techniques on MI EEG-based BCI data.