The feasibility and safety of brain-computer interface (BCI) systems for patients with acute/subacute stroke have not been established. The aim of this study was to firstly demonstrate the feasibility and safety of a bedside BCI system for inpatients with acute/subacute stroke in a small cohort of inpatients. Four inpatients with early-phase hemiplegic stroke (7-24 days from stroke onset) participated in this study. The portable BCI system showed real-time feedback of sensorimotor rhythms extracted from scalp electroencephalograms (EEGs). Patients attempted to extend the wrist on their affected side, and neuromuscular electrical stimulation was applied only when the system detected significant movement intention-related changes in EEG. Between 120 and 200 training trials per patient were successfully and safely conducted at the bedside over 2-4 days. Our results clearly indicate that the proposed bedside BCI system is feasible and safe. Larger clinical studies are needed to determine the clinical efficacy of the system and its effect size in the population of patients with acute/subacute post-stroke hemiplegia.
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http://dx.doi.org/10.1080/10400435.2020.1836067 | DOI Listing |
Sensors (Basel)
December 2024
Department of Computer Science, Faculty of Computing and Information Technology, King Abdulaziz University, Jeddah 21589, Saudi Arabia.
This systematic review examines EEG-based imagined speech classification, emphasizing directional words essential for development in the brain-computer interface (BCI). This study employed a structured methodology to analyze approaches using public datasets, ensuring systematic evaluation and validation of results. This review highlights the feature extraction techniques that are pivotal to classification performance.
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December 2024
College of Computer and Information Sciences (CCIS), King Saud University, Riyadh 11543, Saudi Arabia.
One of the most promising applications for electroencephalogram (EEG)-based brain-computer interfaces (BCIs) is motor rehabilitation through motor imagery (MI) tasks. However, current MI training requires physical attendance, while remote MI training can be applied anywhere, facilitating flexible rehabilitation. Providing remote MI training raises challenges to ensuring an accurate recognition of MI tasks by healthcare providers, in addition to managing computation and communication costs.
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December 2024
College of Design and Innovation, Tongji University, Shanghai 200092, China.
Artificial intelligence (AI) systems are widely applied in various industries and everyday life, particularly in fields such as virtual assistants, healthcare, and education. However, this paper highlights that existing research has often overlooked the philosophical and media aspects. To address this, we developed an interactive system called "Human Nature Test".
View Article and Find Full Text PDFBioengineering (Basel)
November 2024
College of Engineering, Design and Physical Sciences, Brunel University London, London UB8 3PH, UK.
Attention is one of many human cognitive functions that are essential in everyday life. Given our limited processing capacity, attention helps us focus only on what matters. Focusing attention on one speaker in an environment with many speakers is a critical ability of the human auditory system.
View Article and Find Full Text PDFBrain Sci
December 2024
Faculty of Information Technology and Bionics, Pázmány Péter Catholic University, Práter utca 50/a, 1083 Budapest, Hungary.
: Accurately classifying Electroencephalography (EEG) signals is essential for the effective operation of Brain-Computer Interfaces (BCI), which is needed for reliable neurorehabilitation applications. However, many factors in the processing pipeline can influence classification performance. The objective of this study is to assess the effects of different processing steps on classification accuracy in EEG-based BCI systems.
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