The dorsolateral pre-frontal cortex bi-polar error-related potential in a locked-in patient implanted with a daily use brain–computer interface

While brain computer interfaces (BCIs) offer the potential of allowing those suffering from loss of muscle control to once again fully engage with their environment by bypassing the affected motor system and decoding user intentions directly from brain activity, they are prone to errors. One possible avenue for BCI performance improvement is to detect when the BCI user perceives the BCI to have made an unintended action and thus take corrective actions. Error-related potentials (ErrPs) are neural correlates of error awareness and as such can provide an indication of when a BCI system is not performing according to the user’s intentions. Here, we investigate the brain signals of an implanted BCI user suffering from locked-in syndrome (LIS) due to late-stage ALS that prevents her from being able to speak or move but not from using her BCI at home on a daily basis to communicate, for the presence of error-related signals. We first establish the presence of an ErrP originating from the dorsolateral pre-frontal cortex (dLPFC) in response to errors made during a discrete feedback task that mimics the click-based spelling software she uses to communicate. Then, we show that this ErrP can also be elicited by cursor movement errors in a continuous BCI cursor control task. This work represents a first step toward detecting ErrPs during the daily home use of a communications BCI.

Visuo-proprioceptive integration and recalibration with multiple visual stimuli

To organize the plethora of sensory signals from our environment into a coherent percept, our brain relies on the processes of multisensory integration and sensory recalibration. We here asked how visuo-proprioceptive integration and recalibration are shaped by the presence of more than one visual stimulus, hence paving the way to study multisensory perception under more naturalistic settings with multiple signals per sensory modality. We used a cursor-control task in which proprioceptive information on the endpoint of a reaching movement was complemented by two visual stimuli providing additional information on the movement endpoint. The visual stimuli were briefly shown, one synchronously with the hand reaching the movement endpoint, the other delayed. In Experiment 1, the judgments of hand movement endpoint revealed integration and recalibration biases oriented towards the position of the synchronous stimulus and away from the delayed one. In Experiment 2 we contrasted two alternative accounts: that only the temporally more proximal visual stimulus enters integration similar to a winner-takes-all process, or that the influences of both stimuli superpose. The proprioceptive biases revealed that integration—and likely also recalibration—are shaped by the superposed contributions of multiple stimuli rather than by only the most powerful individual one.

Brainwave assistive system for paralyzed individuals

The Brain-Computer Interface (BCI) is a system based on brainwaves that can be used to translate and comprehend the innumerable activities of the brain. Brainwave refers to the bioelectric impulses invariably produced in the human brain during neurotransmission, often measured as the action potential. Moreover, BCI essentially uses the widely studied Electroencephalography (EEG) technique to capture brainwave data. Paralysis generally occurs when there is a disturbance in the central nervous system prompted by a neurodegenerative or unforeseen event. To overcome the obstacles associated with paralysis, this paper on the brainwave-assistive system is based on the BCI incorporated with Internet-of-things. BCI can be implemented to achieve control over external devices and applications. For instance, the process of cursor control, motor control, neuroprosthetics and wheelchair control, etc. In this paper, the OpenBCI Cyton-biosensing board has been used for the collection of the EEG data. The accumulated EEG data is executed subsequently to obtain control over the respective systems in real-time. Hence, it can be concluded that the experiments of the paper support the idea of controlling an interfaced system through the real-time application of EEG data.

Visuo-proprioceptive integration and recalibration with multiple visual stimuli

To organize the plethora of sensory signals from our environment into a coherent percept, our brain relies on the processes of multisensory integration and sensory recalibration. We here asked how visuo-proprioceptive integration and recalibration are shaped by the presence of more than one potentially relevant visual stimulus, hence paving the way to studying multisensory perception under more naturalistic settings with multiple signals per sensory modality. By manipulating the spatio-temporal correspondence between the hand position and two visual stimuli during a cursor-control task, we contrasted two alternative accounts: that only the temporally more proximal signal enters integration and recalibration similar to a winner-takes-all process, or that the influences of both visual signals superpose. Our results show that integration - and likely also recalibration - are shaped by the superposed contributions of multiple stimuli rather than by only individual ones.

Effects of Long-Term Meditation Practices on Sensorimotor Rhythm-Based Brain-Computer Interface Learning

Sensorimotor rhythm (SMR)-based brain–computer interfaces (BCIs) provide an alternative pathway for users to perform motor control using motor imagery. Despite the non-invasiveness, ease of use, and low cost, this kind of BCI has limitations due to long training times and BCI inefficiency—that is, the SMR BCI control paradigm may not work well on a subpopulation of users. Meditation is a mental training method to improve mindfulness and awareness and is reported to have positive effects on one’s mental state. Here, we investigated the behavioral and electrophysiological differences between experienced meditators and meditation naïve subjects in one-dimensional (1D) and two-dimensional (2D) cursor control tasks. We found numerical evidence that meditators outperformed control subjects in both tasks (1D and 2D), and there were fewer BCI inefficient subjects in the meditator group. Finally, we also explored the neurophysiological difference between the two groups and showed that the meditators had a higher resting SMR predictor, more stable resting mu rhythm, and a larger control signal contrast than controls during the task.

Mouse-Less Cursor Control for Quadriplegic and Autistic Patients Using Artificial Intelligence

Artificial intelligence is a field within computer science that attempts to simulate and build enhanced human intelligence into computers, mobiles, and various other machines. It can be termed as a powerful tool that has the capability to process huge sums of information with ease and assess patterns created over a period of time to give significant results or suggestions. It has garnered focus from almost every field from education to healthcare. Broadly, AI applications in healthcare include early detection and diagnosis, suggesting treatments, evaluating progress, medical history, and predicting outcomes. This chapter discussed AI, ASD, and what role AI currently plays in advancing autistic lives including detection, analysis, and treatment of ASD and how AI has been improving healthcare and the existing medical and technology aids available for autistic people. Current and future advancements are discussed and suggested in the direction of improving social abilities and reducing the communication and motor difficulties faced by people with ASD.

Effects of Long-Term Meditation Practices on Sensorimotor Rhythm Based BCI Learning

Sensorimotor rhythm (SMR) based brain-computer interfaces (BCIs) provide an alternative pathway for users to perform motor control using motor imagery (MI). Despite the non-invasiveness, ease of use and low cost, this kind of BCI has limitation due to long training times and BCI inefficiency— where a subpopulation cannot generate decodable EEG signals to perform the control task. Meditation is a mental training method to improve mindfulness and awareness, and is reported to have a positive effect on one’s mental state. Here we investigate the behavioral and electrophysiological differences between experienced meditators and meditation naïve subjects in 1-dimensional and 2-dimensional cursor control tasks. We found that within subjects who have room for improvement, meditators outperformed control subjects in both tasks, and there were fewer BCI insufficient subjects in the meditator group. Finally, we also explored the neurophysiological difference between the two groups, and showed that meditators had higher SMR predictor and were better able to generate decodable EEG signals to achieve SMR BCI control.

Effects of Peripheral Haptic Feedback on Intracortical Brain-Computer Interface Control and Associated Sensory Responses in Motor Cortex

Intracortical brain-computer interfaces (iBCIs) provide people with paralysis a means to control devices with signals decoded from brain activity. Despite recent impressive advances, these devices still cannot approach able-bodied levels of control. To achieve naturalistic control and improved performance of neural prostheses, iBCIs will likely need to include proprioceptive feedback. With the goal of providing proprioceptive feedback via mechanical haptic stimulation, we aim to understand how haptic stimulation affects motor cortical neurons and ultimately, iBCI control. We provided skin shear haptic stimulation as a substitute for proprioception to the back of the neck of a person with tetraplegia. The neck location was determined via assessment of touch sensitivity using a monofilament test kit. The participant was able to correctly report skin shear at the back of the neck in 8 unique directions with 65% accuracy. We found motor cortical units that exhibited sensory responses to shear stimuli, some of which were strongly tuned to the stimuli and well modeled by cosine-shaped functions. We also demonstrated online iBCI cursor control with continuous skin-shear feedback driven by decoded command signals. Cursor control performance increased slightly but significantly when the participant was given haptic feedback, compared to the purely visual feedback condition.