Effects Of Brain-computer Interface Research Articles

Effects of Brain Computer Interface (BCI) rehabilitation robot on upper limb function in chronic stroke patients - A randomized controlled trial

Effects of Brain Computer Interface (BCI) rehabilitation robot on upper limb function in chronic stroke patients - A randomized controlled trial

Real-Time Attention Regulation and Cognitive Monitoring Using a Wearable EEG-based BCI.

Attention regulation is an essential ability in daily life that affects learning and work efficiency and is closely related to mental health. The effectiveness of brain-computer interface (BCI) systems in attention regulation has been proven, but most of these systems rely on bulky and expensive equipment and are still in the experimental stage. This study proposes a wearable BCI system for real-time attention regulation and cognitive monitoring. The BCI system integrates a wearable singlechannel electroencephalogram (EEG) headband with wireless data streaming for real-time analysis. Twenty healthy subjects participated in the long-term attention regulation experiment and were evenly divided into an experimental group and a control group based on the presence of realtime neurofeedback. The neurofeedback is represented by output value of attention, which calculated from singlechannel EEG data. Before and after the regulation sessions, baseline assessments were conducted for each subject, incorporating multi-channel EEG data analysis and cognitive behavioral evaluations, to verify the effectiveness of system for attention regulation. The online experimental results indicate that the average attention level in the experimental group increased from 0.625 to 0.812, while no significant improvement was observed in the control group. Further comparative analysis revealed the reasons for the enhancement of attention regulation ability in terms of both brain network patterns and cognitive performance. The proposed wearable BCI system is effective at improving attention regulation ability and could be a promising tool for assisting people with attention disorders.

Therapeutic Effectiveness of Brain Computer Interfaces in Stroke Patients: A Systematic Review.

Brain-computer interfaces (BCIs) are a rapidly advancing field which utilizes brain activity to control external devices for a myriad of functions, including the restoration of motor function. Clinically, BCIs have been especially impactful in patients who suffer from stroke-mediated damage. However, due to the rapid advancement in the field, there is a lack of accepted standards of practice. Therefore, the aim of this systematic review is to summarize the current literature published regarding the efficacy of BCI-based rehabilitation of motor dysfunction in stroke patients. This systematic review was performed in accordance with the guidelines set forth by the Preferred Reporting Items for Systematic Reviews and Meta-analysis (PRISMA) 2020 statement. PubMed, Embase, and Cochrane Library were queried for relevant articles and screened for inclusion criteria by two authors. All discrepancies were resolved by discussion among both reviewers and subsequent consensus. 11/12 (91.6%) of studies focused on upper extremity outcomes and reported larger initial improvements for participants in the treatment arm (using BCI) as compared to those in the control arm (no BCI). 2/2 studies focused on lower extremity outcomes reported improvements for the treatment arm compared to the control arm. This systematic review illustrates the utility BCI has for the restoration of upper extremity and lower extremity motor function in stroke patients and supports further investigation of BCI for other clinical indications.

EVALUATION OF EFFICIENCY OF USING OF BRAIN-COMPUTER INTERFACE IN LEARNING IMAGINATION OF MOVEMENTS OF UPPER AND LOWER LIMBS

The effectiveness of brain-computer interface (BCI) control and the success of imagination of movement of the upper and lower extremities were evaluated by the accuracy of recognition of EEG signals (classification accuracy) when imagining movements of the hands, feet and locomotion during 10-day training of 10 volunteers. Averaged data of all the volunteers revealed, that, on the first day of training, the classification accuracy is higher when imagining locomotion than foot movements, on the second day – hands than locomotion, on the fifth day – feet than hands. The average values of classification accuracy when imagining movements of the hands and feet increase by the 3rd day of training, further changes are specific depending on which movement is imagined. When learning the imagination of locomotion, the accuracy of classification does not significantly change. An assessment of the dynamics of individual changes in the accuracy of classification according to linear trends showed that in three participants, training led to an increase in the accuracy of classification (of the hand movements and locomotion – in one subject, of feet – in two subjects); in other three participants – to decrease (of the movements of the hands and locomotion – in one subject, of the locomotion – in the second subject, of feet – in the third). The four participants, as well as the sample average, had no significant changes. The results are discussed in terms of changes in the activity of brain structures during learning and depending on the type of imaginary movements.

Effects of brain-computer interface with functional electrical stimulation for gait rehabilitation in multiple sclerosis patients: preliminary findings in gait speed and event-related desynchronization onset latency

Objective. Brain-computer Interfaces (BCI) with functional electrical stimulation (FES) as a feedback device might promote neuroplasticity and hence improve motor function. Novel findings suggested that neuroplasticity could be possible in people with multiple sclerosis (pwMS). This preliminary study explores the effects of using a BCI-FES in therapeutic intervention, as an emerging methodology for gait rehabilitation in pwMS. Approach. People with relapsing-remitting, primary progressive or secondary progressive MS were evaluated with the inclusion criteria to enroll the nine participants required by the statistically computed sample size. Each patient trained with a BCI-FES during 24 sessions distributed in eight weeks. The effects were evaluated on gait speed (Timed 25 Foot Walk), walking ability (12-item Multiple Sclerosis Walking Scale), quality of life measures, the true positive rate as the BCI-FES performance metric and the event-related desynchronization (ERD) onset latency of the sensorimotor rhythms. Main results. Seven patients completed the therapeutic intervention. A statistically and clinically significant post-treatment improvement was observed in gait speed, as a result of a reduction in the time to walk 25 feet (−1.99 s, p = 0.018), and walking ability (−31.25 score points, p = 0.028). The true positive rate showed a statistically significant improvement (+15.87 score points, p = 0.018). An earlier ERD onset latency (−180 ms) after treatment was found. Significance. This is the first study that explored gait rehabilitation using BCI-FES in pwMS. The results showed improvement in gait which might have been promoted by changes in functional brain connections involved in sensorimotor rhythm modulation. Although more studies with a larger sample size and control group are required to validate the efficacy of this approach, these results suggest that BCI-FES technology could have a positive effect on MS gait rehabilitation.

Effects of Brain Computer Interface based Functional Electrical Stimulation on Muscle Strength, Balance, and Gait Applied to Gluteus Medius in Stroke Patients

Effects of Brain Computer Interface based Functional Electrical Stimulation on Muscle Strength, Balance, and Gait Applied to Gluteus Medius in Stroke Patients

Classification of single-trial motor imagery EEG by complexity regularization

Brain computer interface based on electroencephalogram is a popular way to enable communication between brain and output devices helping elderly and disabled people and in rehabilitation. In practice, the effectiveness of brain computer interface has a strong relationship with the classification accuracy of single trials. Common spatial pattern is believed to be an effective algorithm for classifying the single-trial brain signal. Since it is based on the characteristics of a broad frequency band which is manually selected and not individual variability, it is sensitive to noise and individual variability. In this paper, the common spatial pattern was extended in order to improve classification accuracies and to mitigate these influences. The channel-specific complexity weights of characteristic on montage were derived and added to improve the effects of the relevant function area and the separability between classes. The proposed method was evaluated using two public datasets, and achieved an average accuracy of 18.4% higher than conventional common spatial pattern, and the performance of the proposed method over conventional common spatial pattern was significant (p < 0.05). It indicates that the proposed method extracts subject-specific characteristics and outperforms the conventional common spatial pattern in single-trial EEG classification.