A Brain Controlled Command-Line Interface to Enhance the Accessibility of Severe Motor Disabled People to Personnel Computer

Abstract

There are many applications controlled by the brain signals to bridge the gap in the digital divide between the disabled and the non-disabled people. The deployment of novel assistive technologies using brain-computer interface (BCI) will go a long way toward achieving this lofty goal, especially after the successes demonstrated by these technologies in the daily life of people with severe disabilities. This paper contributes in this direction by proposing an integrated framework to control the operating system functionalities using Electroencephalography signals. Different signal processing algorithms were applied to remove artifacts, extract features, and classify trials. The proposed approach includes different classification algorithms dedicated to detecting the P300 responses efficiently. The predicted commands passed through a socket to the API system, permitting the control of the operating system functionalities. The proposed system outperformed those obtained by the winners of the BCI competition and reached an accuracy average of 94.5% according to the offline approach. The framework was evaluated according to the online process and achieved an excellent accuracy attaining 97% for some users but not less than 90% for others. The suggested framework enhances the information accessibility for people with severe disabilities and helps them perform their daily tasks efficiently. It permits the interaction between the user and personal computers through the brain signals without any muscular efforts.

Classification Strategies for P300-Based BCI-Spellers Adopting the Row Column Paradigm

“…Various classifiers are applied during the first phase of the classification problem to analyze and select the appropriate one for our application. For example, we have evaluated the linear discriminant analysis (LDA) [ 35 ], partial least squares regression (PLS) [ 36 ], logistic regression (REG) [ 37 ], and support vector machine (SVM) [ 19 ]. These algorithms were evaluated using the BCI competition dataset containing the recording of two subjects, A and B.…”

“…Thus, to detect and record these deflections, the EEG electrodes should cover the brain’s parietal lobe. Many prototypes have adopted this paradigm [ 19 , 21 , 22 , 23 , 24 , 25 , 26 , 27 , 28 ]. They are all characterized by a high degree of accuracy and require a short time of training.…”

“…The current work has been conducted within a funded research project which aims to promote the accessibility of people with severe disabilities by developing a series of integrated Brain controlled tools such as mail–client, Web browser and command–line interface [ 19 ]. The developed prototypes adopted a P300-based BCI-speller.…”

Using Brain-Computer Interface to improve learning skills for students with disabilities: a rapid review

“…• Control of external devices, such as limbs prostheses [18] • Smart home environments [19] • Robots and Exoskeletons [20] • Robotic hand [21] • Hearing prostheses [22] • Wheelchairs [23] • Computer programs [24] • Virtual reality, avatars, and metaverse [25] • Virtual environment and smart cities [26] BCI's most important use is to give individuals intuitive control over overreaching and grasping movements using their paralyzed limbs [27]. Additional possible applications include communication [28].…”

Utilizing Brain-Computer Interface to Enhance the learning Capacities of Individuals with Impairments: A Brief Review

“… Control of external devices, such as limbs prostheses [18]  Smart home environments [19]  Robots and Exoskeletons [20]  Robotic hand [21]  Hearing prostheses [22]  Wheelchairs [23]  Computer programs [24]  Virtual reality, avatars, and metaverse [25]  Virtual environment and smart cities [26] The most essential use of BCI is to offer paralyzed persons intuitive control over overreaching and gripping actions [27]. Additional conceivable uses include communication [28].…”