Use of Imaginary Lower Limb Movements to Control Brain–Computer Interface Systems

Real-Time Home Automation System Using BCI Technology

A Brain-Computer Interface (BCI) processes and converts brain signals to provide commands to output devices to carry out certain tasks. The main purpose of BCIs is to replace or restore the missing or damaged functions of disabled people, including in neuromuscular disorders like Amyotrophic Lateral Sclerosis (ALS), cerebral palsy, stroke, or spinal cord injury. Hence, a BCI does not use neuromuscular output pathways; it bypasses traditional neuromuscular pathways by directly interpreting brain signals to command devices. Scientists have used several techniques like electroencephalography (EEG) and intracortical and electrocorticographic (ECoG) techniques to collect brain signals that are used to control robotic arms, prosthetics, wheelchairs, and several other devices. A non-invasive method of EEG is used for collecting and monitoring the signals of the brain. Implementing EEG-based BCI technology in home automation systems may facilitate a wide range of tasks for people with disabilities. It is important to assist and empower individuals with paralysis to engage with existing home automation systems and gadgets in this particular situation. This paper proposes a home security system to control a door and a light using an EEG-based BCI. The system prototype consists of the EMOTIV Insight™ headset, Raspberry Pi 4, a servo motor to open/close the door, and an LED. The system can be very helpful for disabled people, including arm amputees who cannot close or open doors or use a remote control to turn on or turn off lights. The system includes an application made in Flutter to receive notifications on a smartphone related to the status of the door and the LEDs. The disabled person can control the door as well as the LED using his/her brain signals detected by the EMOTIV Insight™ headset.

Enhanced lower-limb motor imagery by kinesthetic illusion

Brain-computer interface (BCI) based on lower-limb motor imagery (LMI) enables hemiplegic patients to stand and walk independently. However, LMI ability is usually poor for BCI-illiterate (e.g., some stroke patients), limiting BCI performance. This study proposed a novel LMI-BCI paradigm with kinesthetic illusion(KI) induced by vibratory stimulation on Achilles tendon to enhance LMI ability. Sixteen healthy subjects were recruited to carry out two research contents: (1) To verify the feasibility of induced KI by vibrating Achilles tendon and analyze the EEG features produced by KI, research 1 compared the subjective feeling and brain activity of participants during rest task with and without vibratory stimulation (V-rest, rest). (2) Research 2 compared the LMI-BCI performance with and without KI (KI-LMI, no-LMI) to explore whether KI enhances LMI ability. The analysis methods of both experiments included classification accuracy (V-rest vs. rest, no-LMI vs. rest, KI-LMI vs. rest, KI-LMI vs. V-rest), time-domain features, oral questionnaire, statistic analysis and brain functional connectivity analysis. Research 1 verified that induced KI by vibrating Achilles tendon might be feasible, and provided a theoretical basis for applying KI to LMI-BCI paradigm, evidenced by oral questionnaire (Q1) and the independent effect of vibratory stimulation during rest task. The results of research 2 that KI enhanced mesial cortex activation and induced more intensive EEG features, evidenced by ERD power, topographical distribution, oral questionnaire (Q2 and Q3), and brain functional connectivity map. Additionally, the KI increased the offline accuracy of no-LMI/rest task by 6.88 to 82.19% (p < 0.001). The simulated online accuracy was also improved for most subjects (average accuracy for all subjects: 77.23% > 75.31%, and average F1_score for all subjects: 76.4% > 74.3%). The LMI-BCI paradigm of this study provides a novel approach to enhance LMI ability and accelerates the practical applications of the LMI-BCI system.

Decoding EEG rhythms offline and online during motor imagery for standing and sitting based on a brain-computer interface

Motor imagery (MI)-based brain-computer interface (BCI) systems have shown promising advances for lower limb motor rehabilitation. The purpose of this study was to develop an MI-based BCI for the actions of standing and sitting. Thirty-two healthy subjects participated in the study using 17 active EEG electrodes. We used a combination of the filter bank common spatial pattern (FBCSP) method and the regularized linear discriminant analysis (RLDA) technique for decoding EEG rhythms offline and online during motor imagery for standing and sitting. The offline analysis indicated the classification of motor imagery and idle state provided a mean accuracy of 88.51 ± 1.43% and 85.29 ± 1.83% for the sit-to-stand and stand-to-sit transitions, respectively. The mean accuracies of the sit-to-stand and stand-to-sit online experiments were 94.69 ± 1.29% and 96.56 ± 0.83%, respectively. From these results, we believe that the MI-based BCI may be useful to future brain-controlled standing systems.

Success of Hand Movement Imagination Depends on Personality Traits, Brain Asymmetry, and Degree of Handedness

Brain-computer interfaces (BCIs), based on motor imagery, are increasingly used in neurorehabilitation. However, some people cannot control BCI, predictors of this are the features of brain activity and personality traits. It is not known whether the success of BCI control is related to interhemispheric asymmetry. The study was conducted on 44 BCI-naive subjects and included one BCI session, EEG-analysis, 16PF Cattell Questionnaire, estimation of latent left-handedness, and of subjective complexity of real and imagery movements. The success of brain states recognition during imagination of left hand (LH) movement compared to the rest is higher in reserved, practical, skeptical, and not very sociable individuals. Extraversion, liveliness, and dominance are significant for the imagination of right hand (RH) movements in "pure" right-handers, and sensitivity in latent left-handers. Subjective complexity of real LH and of imagery RH movements correlates with the success of brain states recognition in the imagination of movement of LH compared to RH and depends on the level of handedness. Thus, the level of handedness is the factor influencing the success of BCI control. The data are supposed to be connected with hemispheric differences in motor control, lateralization of dopamine, and may be important for rehabilitation of patients after a stroke.

A multi-modal modified feedback self-paced BCI to control the gait of an avatar

Brain-computer interfaces (BCI) have been used to control the gait of a virtual self-avatar with a proposed application in the field of gait rehabilitation.

OBJECTIVE

to develop a high performance multi-modal BCI to control single steps and forward walking of an immersive virtual reality avatar. This system will overcome the limitation of existing systems.

APPROACH

This system used MI of these actions, in cue-paced and self-paced modes. Twenty healthy participants participated in this 4 sessions study across 4 different days. They were cued to imagine a single step forward with their right or left foot, or to imagine walking forward. They were instructed to reach a target by using the MI of multiple steps (self-paced switch-control mode) or by maintaining MI of forward walking (continuous-control mode). The movement of the avatar was controlled by two calibrated RLDA classifiers that used the µ power spectral density (PSD) over the foot area of the motor cortex as a feature. The classifiers were retrained after every session. For a subset of the trials, positive modified feedback was presented to half of the participants.

MAIN RESULTS

All participants were able to operate the BCI. Their average offline performance, after retraining the classifiers was 86.0 ± 6.1%, showing that the recalibration of the classifiers enhanced the offline performance of the BCI (p < 0.01). The average online performance was 85.9 ± 8.4% showing that modified feedback enhanced BCI performance (p =0.001). The average performance was 83% at self-paced switch control and 92% at continuous control mode.

SIGNIFICANCE

This study reports on the first novel integration of different design approaches, different control modes and different performance enhancement techniques, all in parallel in one single high performance and multi-modal BCI system, to control single steps and forward walking of an immersive virtual reality avatar.