Free Virtual Navigation Using Motor Imagery Through an Asynchronous Brain–Computer Interface

2010 ◽  
Vol 19 (1) ◽  
pp. 71-81 ◽  
Author(s):  
Francisco Velasco-Álvarez ◽  
Ricardo Ron-Angevin ◽  
Maria José Blanca-Mena
Keyword(s):  
Virtual Environments ◽  
Motor Imagery ◽  
Brain Computer Interface ◽  
Computer Interface ◽  
Imagery Task ◽  
Virtual Navigation ◽  
Intentional Control ◽  
Feedback Training ◽  
Control Interface ◽  
Fixed Path

In this paper, an asynchronous brain–computer interface is presented that enables the control of a wheelchair in virtual environments using only one motor imagery task. The control is achieved through a graphical intentional control interface with three navigation commands (move forward, turn right, and turn left) which are displayed surrounding a circle. A bar is rotating in the center of the circle, so it points successively to the three possible commands. The user can, by motor imagery, extend this bar length to select the command at which the bar is pointing. Once a command is selected, the virtual wheelchair moves in a continuous way, so the user controls the length of the advance or the amplitude of the turns. Users can voluntarily switch from this interface to a noncontrol interface (and vice versa) when they do not want to generate any command. After performing a cue-based feedback training, three subjects carried out an experiment in which they had to navigate through the same fixed path to reach an objective. The results obtained support the viability of the system.

scholarly journals Multiclass Informative Instance Transfer Learning Framework for Motor Imagery-Based Brain-Computer Interface

2018 ◽  
Vol 2018 ◽  
pp. 1-12 ◽  
Author(s):  
Ibrahim Hossain ◽  
Abbas Khosravi ◽  
Imali Hettiarachchi ◽  
Saeid Nahavandi
Keyword(s):  
Transfer Learning ◽  
Motor Imagery ◽  
Brain Computer Interface ◽  
Training Session ◽  
Training Data ◽  
Superior Performance ◽  
Computer Interface ◽  
Imagery Task ◽  
Learning Framework ◽  
Optimal Ensemble

A widely discussed paradigm for brain-computer interface (BCI) is the motor imagery task using noninvasive electroencephalography (EEG) modality. It often requires long training session for collecting a large amount of EEG data which makes user exhausted. One of the approaches to shorten this session is utilizing the instances from past users to train the learner for the novel user. In this work, direct transferring from past users is investigated and applied to multiclass motor imagery BCI. Then, active learning (AL) driven informative instance transfer learning has been attempted for multiclass BCI. Informative instance transfer shows better performance than direct instance transfer which reaches the benchmark using a reduced amount of training data (49% less) in cases of 6 out of 9 subjects. However, none of these methods has superior performance for all subjects in general. To get a generic transfer learning framework for BCI, an optimal ensemble of informative and direct transfer methods is designed and applied. The optimized ensemble outperforms both direct and informative transfer method for all subjects except one in BCI competition IV multiclass motor imagery dataset. It achieves the benchmark performance for 8 out of 9 subjects using average 75% less training data. Thus, the requirement of large training data for the new user is reduced to a significant amount.

scholarly journals Brain-computer interface algorithm based on wavelet-phase stability analysis in motor imagery experiment

2020 ◽  
Vol 16 (2) ◽  
pp. 236-242
Author(s):  
Muhamad Firdaus Mohd Rafi ◽  
Arief Ruhullah A Harris ◽  
Tan Tian Swee ◽  
Kah Meng Leong ◽  
Jia Hou Tan ◽  
...  
Keyword(s):  
Motor Imagery ◽  
Phase Stability ◽  
Phase Synchronization ◽  
Brain Computer Interface ◽  
Computer Interface ◽  
Imagery Task ◽  
Body Parts ◽  
Time Data ◽  
Input Signals ◽  
Human Augmentation

Severe movement or motor disability diseases such as amyotrophic lateral sclerosis (ALS), cerebral palsy (CB), and muscular dystrophy (MD) are types of diseases which lead to the total of function loss of body parts, usually limbs. Patient with an extreme motor impairment might suffers a locked-in state, resulting in the difficulty to perform any physical movements. These diseases are commonly being treated by a specific rehabilitation procedure with prescribed medication. However, the recovery process is time-consuming through such treatments. To overcome these issues, Brain-Computer Interface system is introduced in which one of its modalities is to translate thought via electroencephalography (EEG) signals by the user and generating desired output directly to an external artificial control device or human augmentation. Here, phase synchronization is implemented to complement the BCI system by analyzing the phase stability between two input signals. The motor imagery-based experiment involved ten healthy subjects aged from 24 to 30 years old with balanced numbers between male and female. Two aforementioned input signals are the respective reference data and the real time data were measured by using phase stability technique by indicating values range from 0 (least stable) to 1 (most stable). Prior to that, feature extraction was utilized by applying continuous wavelet transform (CWT) to quantify significant features on the basis of motor imagery experiment which are right and left imaginations. The technique was able to segregate different classes of motor imagery task based on classification accuracy. This study affirmed the approach’s ability to achieve high accuracy output measurements.

scholarly journals A Link between the Increase in Electroencephalographic Coherence and Performance Improvement in Operating a Brain-Computer Interface

2015 ◽  
Vol 2015 ◽  
pp. 1-11 ◽  
Author(s):  
Irma Nayeli Angulo-Sherman ◽  
David Gutiérrez
Keyword(s):  
Performance Improvement ◽  
Motor Imagery ◽  
Brain Computer Interface ◽  
Training Session ◽  
Brain Regions ◽  
Computer Interface ◽  
Imagery Task ◽  
And Performance ◽  
The Relationship ◽  
Event Related Coherence

We study the relationship between electroencephalographic (EEG) coherence and accuracy in operating a brain-computer interface (BCI). In our case, the BCI is controlled through motor imagery. Hence, a number of volunteers were trained using different training paradigms: classical visual feedback, auditory stimulation, and functional electrical stimulation (FES). After each training session, the volunteers’ accuracy in operating the BCI was assessed, and the event-related coherence (ErCoh) was calculated for all possible combinations of pairs of EEG sensors. After at least four training sessions, we searched for significant differences in accuracy and ErCoh using one-way analysis of variance (ANOVA) and multiple comparison tests. Our results show that there exists a high correlation between an increase in ErCoh and performance improvement, and this effect is mainly localized in the centrofrontal and centroparietal brain regions for the case of our motor imagery task. This result has a direct implication with the development of new techniques to evaluate BCI performance and the process of selecting a feedback modality that better enhances the volunteer’s capacity to operate a BCI system.

Sign in / Sign up

Export Citation Format

Share Document