Recent Trends in EEG-Based Motor Imagery Signal Analysis and Recognition: A Comprehensive Review

Abstract

The electroencephalogram (EEG) motor imagery (MI) signals are the widespread paradigms in the brain-computer interface (BCI). Its significant applications in the gaming, robotics, and medical fields drew our attention to perform a detailed analysis. However, the problem is ill-posed as these signals are highly nonlinear, unpredictable, and noisy, hence making it exceedingly hard to be analyzed adequately. This paper provides a first-of-its-kind comprehensive review of conventional signal processing and deep learning techniques for BCI MI signal analysis. The review comprises extensive works carried out in the domain in the recent past, highlighting the current challenges of the problem. A new categorization of the existing approaches has been presented for better clarification. An all-inclusive description of the signal processing techniques has been corroborated by relevant works in the area. Moreover, architectures of various standard deep learning algorithms along with their merits and demerits are also explicated to assist the readers. The tabular representations of the numerical results are also readily provided. This work also presents the open research problems and future directions.

Multi-domain feature joint optimization based on multi-view learning for improving the EEG decoding

“…By not relying on conventional brain output pathways, BCI opens up entirely new ways for the human brain to communicate and control information with the outside world ( Maslova et al, 2023 ). There are many patterns of brain electrical signals in BCI: electroencephalogram (EEG), electrocorticography (ECoG), functional magnetic resonance imaging (fMRI), and positron emission tomography (PET) ( Sharma et al, 2023 ). The EEG is extensively applied to collect brain signals in BCI systems since it is inexpensive, portable, and non-invasive, and has relatively high temporal resolution ( McFarland and Wolpaw, 2017 ; Padfield et al, 2019 ).…”

An Efficient Approach for Recognition of Motor Imagery EEG Signals Using the Fourier Decomposition Method

“…The primary goal of BCI is to use brain-generated electrical signals to control the setting outside the brain [2]. To measure these signals, a non-invasive, portable, low-cost, and high-temporal resolution technique known as electroencephalography (EEG) is employed [3]. Among all the brain signals, sensory-motor rhythms in the brain are responsible for motor-related activities.…”