Brain computer interface based applications for training and rehabilitation of students with neurodevelopmental disorders. A literature review

Abstract

The aim of this article is to explore a paradigm shift on Brain Computer Interface (BCI) research, as well as on intervention best practices for training and rehabilitation of students with neurodevelopmental disorders. Recent studies indicate that BCI devices have positive impact on students' attention skills and working memory as well as on other skills, such as visuospatial, social, imaginative and emotional abilities. BCI applications aim to emulate humans' brain and address the appropriate understanding for each student's neurodevelopmental disorders. Studies conducted to provide knowledge about BCI-based intervention applications regarding memory, attention, visuospatial, learning, collaboration, and communication, social, creative and emotional skills are highlighted. Only non-invasive BCI type of applications are being investigated based upon representative, non-exhaustive and state-of-the-art studies within the field. This article examines the progress of BCI research so far, while different BCI paradigms are investigated. BCI-based applications could successfully regulate students' cognitive abilities when used for their training and rehabilitation. Future directions to investigate BCI-based applications for training and rehabilitation of students with neurodevelopmental disorders concerning the different populations involved are discussed.

Motor Training Using Mental Workload (MWL) With an Assistive Soft Exoskeleton System: A Functional Near-Infrared Spectroscopy (fNIRS) Study for Brain–Machine Interface (BMI)

“…In ecological settings, the BCI systems face numerous challenges such as low BCI signal strength, low data transfer rate, and high error percentage due to high brain signal variance (Kameswara et al, 2012 ; Ramadan et al, 2015 ). The accuracy of BCI system is also effected and sometimes reduced owing to the lack of ability of the patient to retain similar cognitive states in various sessions as it is observed that long intervals of usage introduce cognitive fatigue for the patients (Papanastasiou et al, 2020 ). The tasks in this study were designed for potential stroke patients who may generate control commands even under stress and cognitive load.…”

“…Therefore, BCI systems are mostly designed as user specific and according to the abilities and capabilities of patients in a specific environment (Tariq et al, 2018 ). The BCI system's accurateness is also effected and decreased owing to the lack of patient's ability to hold and retain similar cognitive mental states in various experimental trials conducted in few studies as it is ascertained during these studies that that long periods of procedure may introduce mental cognitive fatigue for the patients (Papanastasiou et al, 2020 ). The applications of BCI systems are modest in clinical and medical environments owing to various factors like the BCI accuracy, BCI reliability of sensory system interface and control translation algorithms that encompasses constraints like time, ITR, the number of optodes or electrodes, and the number of distinguished emotions (Al-Nafjan et al, 2017 ).…”

An Introductory Tutorial on Brain–Computer Interfaces and Their Applications

“…Hence, the system could rely on an average based on eight trials, which is more than sufficient for adequate classification, even though each subject contributed only one trial. The paper in [107] presents a detailed review about BCI applications for training and rehabilitation of students with neurodevelopmental disorders.…”

Toward EEG-Based BCI Applications for Industry 4.0: Challenges and Possible Applications

“…Another emerging BCI application-also relying on passive BCI technique-concerns adaptive training, which might reinforce the learning process of complex industrial procedures. This neurofeedback approach is already used in clinical settings, to support learning or rehabilitate attention in children with neurodevelopmental disorders (e.g., Papanastasiou et al, 2020). Indeed, such monitored training would allow boosting attentional processes while adapting task difficulty according to cognitive load or vigilance, to optimize learning and prevent frustration.…”