Evaluating a Semiautonomous Brain-Computer Interface Based on Conformal Geometric Algebra and Artificial Vision

Abstract

In this paper, we evaluate a semiautonomous brain-computer interface (BCI) for manipulation tasks. In such a system, the user controls a robotic arm through motor imagery commands. In traditional process-control BCI systems, the user has to provide those commands continuously in order to manipulate the effector of the robot step-by-step, which results in a tiresome process for simple tasks such as pick and replace an item from a surface. Here, we take a semiautonomous approach based on a conformal geometric algebra model that solves the inverse kinematics of the robot on the fly, and then the user only has to decide on the start of the movement and the final position of the effector (goal-selection approach). Under these conditions, we implemented pick-and-place tasks with a disk as an item and two target areas placed on the table at arbitrary positions. An artificial vision (AV) algorithm was used to obtain the positions of the items expressed in the robot frame through images captured with a webcam. Then, the AV algorithm is integrated into the inverse kinematics model to perform the manipulation tasks. As proof-of-concept, different users were trained to control the pick-and-place tasks through the process-control and semiautonomous goal-selection approaches so that the performance of both schemes could be compared. Our results show the superiority in performance of the semiautonomous approach as well as evidence of less mental fatigue with it.

Evaluation of a Fast Test Based on Biometric Signals to Assess Mental Fatigue at the Workplace—A Pilot Study

“…The questions and possible answers from the FAS are presented in Figure S1 (see Supplemental Material). According to [71], the FAS total score can be classified into three classes: no fatigue (1-21), substantial fatigue (22)(23)(24)(25)(26)(27)(28)(29)(30)(31)(32)(33)(34)(35) and extreme fatigue (36)(37)(38)(39)(40)(41)(42)(43)(44)(45)(46)(47)(48)(49)(50). From the 17 participants, 3 were in the no fatigue class (19 ± 2), 8 in substantial fatigue (27 ± 3), and 6 (37 ± 3) in extreme fatigue.…”

“…There are also studies on time-locked event-related potentials (ERP) in EEG to observe changes in temporal aspects of brain response in fatigued users [46,47]. The P300 waveform is widely used in a BCI context, as well as for mental fatigue evaluation [48][49][50]. The P300 is a positive peak presented approximately 300 ms after the onset of a stimulus [51].…”

A Novel Time‐Incremental End‐to‐End Shared Neural Network with Attention‐Based Feature Fusion for Multiclass Motor Imagery Recognition

“…The purpose of MI-BCI is to identify the imagined movements by classifying the electroencephalogram (EEG) characteristics of the brain, to control the external devices, such as robots [ 3 , 4 ]. On the one hand, MI-BCI can help patients with severe dysfunction and establish communication channels with the outside world.…”

“…In contrast, the phenomenon of a manifest amplitude increase of mu and beta oscillation, which appears in resting or inert states, is called eventrelated synchronization (ERS) [2]. e purpose of MI-BCI is to identify the imagined movements by classifying the electroencephalogram (EEG) characteristics of the brain, to control the external devices, such as robots [3,4]. On the one hand, MI-BCI can help patients with severe dysfunction and establish communication channels with the outside world.…”

“…However, the high complexity and instability of the EEG signals make the feature extraction and pattern classification of signals very challenging. e very important part of the MI-BCI system is how to classify the EEG characteristics of MI task correctly and convert it into external control instructions [4]. At present, the traditional MI-EEG signal feature extraction is mainly based on ERD/ERS in the µ band (8)(9)(10)(11)(12) and the β band (16)(17)(18)(19)(20)(21)(22)(23)(24)(25)(26)(27)(28)(29)(30)(31), including signal bandpass filtering [7], autoregressive model [8], frequency domain statistics [9], phase-locking value (PLV) [10], wavelet transformation and wavelet-packet transformation [11,12], information entropy [13], and common spatial pattern (CSP) [14].…”