Indoor Simulated Training Environment for Brain-Controlled Wheelchair Based on Steady-State Visual Evoked Potentials

Abstract

Brain-controlled wheelchair (BCW) has the potential to improve the quality of life for people with motor disabilities. A lot of training is necessary for users to learn and improve BCW control ability and the performances of BCW control are crucial for patients in daily use. In consideration of safety and efficiency, an indoor simulated training environment is built up in this paper to improve the performance of BCW control. The indoor simulated environment mainly realizes BCW implementation, simulated training scenario setup, path planning and recommendation, simulated operation, and scoring. And the BCW is based on steady-state visual evoked potentials (SSVEP) and the filter bank canonical correlation analysis (FBCCA) is used to analyze the electroencephalography (EEG). Five tasks include individual accuracy, simple linear path, obstacles avoidance, comprehensive steering scenarios, and evaluation task are designed, 10 healthy subjects were recruited and carried out the 7-days training experiment to assess the performance of the training environment. Scoring and command-consuming are conducted to evaluate the improvement before and after training. The results indicate that the average accuracy is 93.55% and improves from 91.05% in the first stage to 96.05% in the second stage (p = 0.001). Meanwhile, the average score increases from 79.88 in the first session to 96.66 in the last session and tend to be stable (p < 0.001). The average number of commands and collisions to complete the tasks decreases significantly with or without the approximate shortest path (p < 0.001). These results show that the performance of subjects in BCW control achieves improvement and verify the feasibility and effectiveness of the proposed simulated training environment.

Toward a Brain-Computer Interface- and Internet of Things-Based Smart Ward Collaborative System Using Hybrid Signals

“…Generally, an EOG signal exhibits obvious patterns that are relatively simple to detect, and consequently, a BCI system based on EOG imposes a low workload and simple operation requirements [ 37 ]. Conversely, BCI systems based on ERPs (P300 and SSEVP) [ 13 , 16 , 22 ]and MI-based BCI systems often have high workloads and require a long training time. In addition, an ERP-based BCI typically has a low ITR or high FOR in either the control or idle state, while an MI BCI commonly has a long RT.…”

Brain-Controlled Multi-Robot at Servo-Control Level Based on Nonlinear Model Predictive Control

“…Considering the potential application values of braincontrolled multi-robot systems at the servo-control level, in our previous conference paper [19] , we introduced a BCI into a multi-robot system to develop a brain-controlled multi-robot system. However, the brain-controlled multi-robot system is simple and basic.…”

“…Thus, expanded from our previous work in Ref. [19], this paper explores how to control the direction, speed, and formation of a multi-robot system via electroencephalographic (EEG) signals and builds a complete brain-controlled multi-robot simulation and physical system. In particular, we propose a novel multi-robot predictive control framework (MRPCF), which can track users' control intentions and ensure the safety of multi-robots.…”

Enhancing SSVEP-Based Brain-Computer Interface with Two-Step Task-Related Component Analysis

“…EEG-based BCI may help severely disabled people, which is used in rehabilitative applications and the internet of medical things (IoMT) [ 2 , 3 ]. Typically, in years past, real-time BCI applications such as brain-controlled vehicles (BCVs) [ 4 ] and brain-controlled wheelchairs (BCWs) [ 5 ] that can be facilitated in daily life have received enormous attention. To control these applications, in the BCI study, EEG signals can be divided into different forms depending on the purpose of use, its type, and so on.…”

“…Among those forms, steady-state visual evoked potential (SSVEP) has attracted much attention due to the high communication rate, classification accuracy, and high signal-to-noise ratio (SNR) [ 6 , 7 ]. Driven by these advantages, the number of SSVEP-based real-time BCI applications have resulted in remarkable achievements [ 4 , 5 , 8 , 9 ].…”