Advances in brain computer interface for amyotrophic lateral sclerosis communication

Brain‐X, Journal Year: 2025, Volume and Issue: 3(1)

Published: March 1, 2025

Abstract Amyotrophic lateral sclerosis (ALS) is a progressive neurodegenerative disease that often results in the loss of speech, creating significant communication barriers. Brain–computer interfaces (BCIs) provide transformative solution for restoring and enhancing quality life ALS individuals. Recent advances implantable electrocorticographic systems have demonstrated feasibility synthesizing intelligible speech directly from neural activity. By recording high‐resolution signals motor, premotor, somatosensory cortices with decoding algorithms, these can transform patterns into acoustic features providing natural intuitive pathways Non‐invasive electroencephalography, while lacking spatial resolution systems, offers safer alternative high temporal capturing speech‐related dynamics. When combined robust feature extraction techniques, such as common pattern time‐frequency analyses, well multimodal integration functional near‐infrared spectroscopy or electromyography, it effectively enhances accuracy system robustness. Despite progress, challenges remain, including user variability, BCI illiteracy, impact fatigue on performance. Personalized models, adaptive secure frameworks brain data privacy are essential addressing limitations, enabling BCIs to enhance accessibility reliability. Advancing technologies methodologies holds immense promise independence bridging gap individuals ALS. Future research could focus long‐term clinical studies evaluate stability effectiveness development more unobtrusive paradigms.

Language: Английский

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Exploring the Intersection of Brain-Computer Interfaces and Traditional, Complementary, and Integrative Medicine

Integrative Medicine Research, Journal Year: 2025, Volume and Issue: 14(2), P. 101142 - 101142

Published: April 2, 2025

Brain-computer interfaces (BCIs) represent a transformative innovation in healthcare, enabling direct communication between the brain and external devices. This educational article explores potential intersection of BCIs traditional, complementary, integrative medicine (TCIM). have shown promise enhancing mind-body practices such as meditation, while their integration with energy-based therapies may offer novel insights measurable outcomes. Emerging advancements, including artificial intelligence-enhanced BCIs, hold for improving personalization expanding therapeutic efficacy TCIM interventions. Despite these opportunities, integrating presents considerable ethical, cultural, practical challenges. Concerns related to informed consent, cultural sensitivity, data privacy, accessibility, regulatory frameworks must be addressed ensure responsible implementation. Interdisciplinary collaboration among relevant stakeholders, conventional practitioners, researchers, policymakers other stakeholders is crucial developing healthcare models that balance patient safety respect diverse healing traditions. Future directions include evidence bases validate through BCI-enhanced research, fostering equitable access neurotechnological promoting global ethical guidelines navigate complex sociocultural dynamics. revolutionize TCIM, offering solutions health challenges more inclusive, approach provided they are utilized responsibly ethically.

Language: Английский

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EEG Signal Prediction for Motor Imagery Classification in Brain–Computer Interfaces

Sensors, Journal Year: 2025, Volume and Issue: 25(7), P. 2259 - 2259

Published: April 3, 2025

Brain-computer interfaces (BCIs) based on motor imagery (MI) generally require EEG signals recorded from a large number of electrodes distributed across the cranial surface to achieve accurate MI classification. Not only does this entail long preparation times and high costs, but it also carries risk losing valuable information when an electrode is damaged, further limiting its practical applicability. In study, signal prediction-based method proposed accuracy in classification using small electrodes. The prediction model was constructed elastic net regression technique, allowing for estimation 22 complete channels just 8 centrally located channels. predicted were used feature extraction results obtained indicate notable efficacy method, showing average performance 78.16% accuracy. demonstrated superior compared traditional approach that few-channel achieved better than full-channel EEG. Although varies among subjects, 62.30% impressive 95.24%, these data capability provide estimates reduced set This highlights potential be implemented MI-based BCI applications, thereby mitigating time cost constraints associated with systems density

Language: Английский

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Enhancing user experience in c-VEP-based BCI: Effects of visual stimulus opacity on performance and visual fatigue

Biomedical Signal Processing and Control, Journal Year: 2025, Volume and Issue: 108, P. 107894 - 107894

Published: May 1, 2025

Language: Английский

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Considerations and discussions on the clear definition and definite scope of brain-computer interfaces

Frontiers in Neuroscience, Journal Year: 2024, Volume and Issue: 18

Published: Aug. 5, 2024

Brain-computer interface (BCI) is a revolutionizing human-computer interaction with potential applications in both medical and non-medical fields, emerging as cutting-edge trending research direction. Increasing numbers of groups are engaging BCI development. However, recent years, there has been some confusion regarding BCI, including misleading hyped propaganda about even non-BCI technologies being labeled BCI. Therefore, clear definition definite scope for thoroughly considered discussed the paper, based on existing definitions six key or essential components In review, different from previous paradigms neural coding explicitly included provided, user (the brain) clearly identified component system. Different people may have viewpoints well related issues, which article. This review argues that will benefit future commercial applications. It hoped this reduce surrounding promote sustainable development field.

Language: Английский

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User Evaluation of a Shared Robot Control System Combining BCI and Eye Tracking in a Portable Augmented Reality User Interface

Sensors, Journal Year: 2024, Volume and Issue: 24(16), P. 5253 - 5253

Published: Aug. 14, 2024

This study evaluates an innovative control approach to assistive robotics by integrating brain–computer interface (BCI) technology and eye tracking into a shared system for mobile augmented reality user interface. Aimed at enhancing the autonomy of individuals with physical disabilities, particularly those impaired motor function due conditions such as stroke, utilizes BCI interpret intentions from electroencephalography signals identify object focus, thus refining commands. integration seeks create more intuitive responsive robot strategy. The real-world usability was evaluated, demonstrating significant potential improve severe impairments. compared eye-tracking-based alternative areas needing improvement. Although achieved acceptable success rate 0.83 in final phase, effective perfect consistently lower completion times (p<0.001). experience responses favored 11 out 26 questions, no differences remaining subjective fatigue higher use (p=0.04). While performance lagged behind tracking, evaluation supports validity our strategy, showing that it could be deployed suggesting pathway further advancements.

Language: Английский

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The Role of AI in Modern Language Translation and Its Societal Applications: A Systematic Literature Review

Communications in computer and information science, Journal Year: 2024, Volume and Issue: unknown, P. 390 - 404

Published: Nov. 26, 2024

Language: Английский

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