Estimation of Forearm Supination/Pronation Motion Based on EEG Signals to Control an Artificial Arm

Abstract

In recent years, many myoelectric arms that are controlled based on electromyogram (EMG) signals of amputee's stump or residual muscles have been proposed. In the cases of above elbow amputees, however, the muscles which generate the forearm, wrist and hand motions do not remain. Therefore, most myoelectric arms for above elbow amputees have less degree of freedom and its dexterity is relatively poor compared with a human upper-limb. To improve the quality of life of above elbow amputees and to increase their mobility in daily life activities, some additional input signals must be prepared. One of the strong candidates of the additional input signals is an electroencephalogram (EEG) signal. An EEG signal is an electric signal that can be measured along a scalp, so that it can be measured even with an above elbow amputee. In this study, an artificial arm for above elbow amputees is controlled based on EMG and EEG signals. In this paper, the EEG-based motion estimation method is proposed to control the forearm supination/pronation motion of the artificial arm. The angle, angular velocity, and angular acceleration of the forearm motion are estimated under several velocities by using EEG signals.

Brain-machine interfaces for rehabilitation in stroke: A review

“…Such hBMIs include residual muscle activity in the BMI control, and hence, in the contingent connection between perilesional cortical areas and movement related afferent feedback. These enriched hBMI systems can lead to a higher decoding accuracy (Kiguchi & Hayashi, 2012;Leeb, Sagha, Chavarriaga, & Millán, 2011;Li et al, 2017) and more degrees of freedom (Kiguchi, Lalitharatne, & Hayashi, 2013), reflected in a richer and smoother control of actuators. A recent study proposed a biologically-inspired hybrid strategy, involving brain and muscle activity, to control a 7 degrees-of-freedom robotic arm, and demonstrated its viability in a moderately paralyzed stroke patient (Sarasola-Sanz et al, 2017).…”

Towards Control of a Transhumeral Prosthesis with EEG Signals

“…For the alpha band power, CAR-processed EEG data are bandpass filtered through 8–12 Hz, followed by squaring. Use of RMS is also reported in a few studies [ 18 ]. Accordingly, the effectiveness of RMS will also be evaluated in the study.…”

“…CAR-processed EEG signals are passed through a 0.1–2.0 Hz bandpass filter to prepare them for use in classification. Few studies [ 18 , 22 , 23 ] have used delta band EEG features for motion intention identification. The current study considers the delta band power spectrum and it is obtained by passing the CAR-processed EEG data through a 0.1–4.0 Hz bandpass filter.…”

“…However, this method is invasive and some difficulties, related to separating the surface EMG signals from different muscles, remain [ 1 ]. Owing to the deficiencies in existing methods, electroencephalogram (EEG) is becoming popular among researchers [ 18 , 19 ] for identifying human motion intention for prosthesis control. EEG records electrical signals from the surface of the human skull that carry information related to all bodily motions.…”

“…EEG records electrical signals from the surface of the human skull that carry information related to all bodily motions. In [ 18 ], an EEG-based motion estimation method was proposed to control forearm supination/pronation of an artificial arm. Bright et al [ 19 ] proposed a method to control flexion/extension of a prosthetic finger based on EEG signals.…”