EEG-Based BCI System to Detect Fingers Movements

Abstract

The advancement of assistive technologies toward the restoration of the mobility of paralyzed and/or amputated limbs will go a long way. Herein, we propose a system that adopts the brain-computer interface technology to control prosthetic fingers with the use of brain signals. To predict the movements of each finger, complex electroencephalogram (EEG) signal processing algorithms should be applied to remove the outliers, extract features, and be able to handle separately the five human fingers. The proposed method deals with a multi-class classification problem. Our machine learning strategy to solve this problem is built on an ensemble of one-class classifiers, each of which is dedicated to the prediction of the intention to move a specific finger. Regions of the brain that are sensitive to the movements of the fingers are identified and located. The average accuracy of the proposed EEG signal processing chain reached 81% for five subjects. Unlike the majority of existing prototypes that allow only one single finger to be controlled and only one movement to be performed at a time, the system proposed will enable multiple fingers to perform movements simultaneously. Although the proposed system classifies five tasks, the obtained accuracy is too high compared with a binary classification system. The proposed system contributes to the advancement of a novel prosthetic solution that allows people with severe disabilities to perform daily tasks in an easy manner.

Neural Decoding of EEG Signals with Machine Learning: A Systematic Review

“…However, CSP does not always perform perfectly with the SVM algorithm. Earlier studies [ 172 , 175 , 258 ] have compared the performance of ANNs and LDA with SVM. Jia et al [ 175 ] have compared the results obtained by using backpropagation neural network (BPNN) and SVM algorithms and found that BPNN with CSP consistently outperformed SVM.…”

Biosignal-Based Human–Machine Interfaces for Assistance and Rehabilitation: A Survey

“…Hence, they reflect the function of some organs (e.g., brain, muscles, eyes) in the form of electrical activity, providing relevant information about them [44,45]. For this reason, biopotentials are used as control signals in many biomedical HMI applications [25,42,[46][47][48][49][50][51][52][53][54][55][56][57][58][59]. These biosignals have their origin in electrophysiological phenomena associated with biochemical events occurring at a cellular level.…”

“…Clear examples are subjects affected by neuromuscular disorders, such as MD, ALS, MS, SCI, and CP, or even poststroke patients and amputees [5,[18][19][20]. In this scenario, two main targets can be identified: robotic control [14,15,34,49,50,[64][65][66][67][68][69] and prosthetic control [46][47][48][69][70][71][72][73][74][75][76][77][78].…”

“…Regarding prosthetic control, Gao et al developed a motor imagery-based BCI to control a prosthetic leg walking in different terrains, intending to reproduce natural human gait [46]. Gannouni et al proposed a BCI system that can detect finger movements [47]. Fuentes-Gonzalez et al designed an EEG-controlled prosthetic arm [48], similarly to Zhan Hong et al [70].…”

“…In this case, stable electrode placement and skin preparation are required. Wet electrodes need the application of a conductive gel to ensure better contact; on the contrary, dry electrodes are very useful for long-term recordings [47]. Amplification and real-time processing are needed because biosignals have small amplitude and are affected by noise, such as electromagnetic interference, motion artifacts, and crosstalk with other biosignals, and modifications over time (e.g., EMG is susceptible to muscle fatigue).…”