Neurorights, Neurotechnologies and Personal Data: Review of the Challenges of Mental Autonomy DOI Creative Commons
Yan An Cornejo Montoya

Journal of Digital Technologies and Law, Journal Year: 2024, Volume and Issue: 2(3), P. 711 - 728

Published: Nov. 10, 2024

Objective : to present the results of a systematic review research on impact neurotechnology legal concepts and regulatory frameworks, addressing ethical social issues related protection individual rights, privacy mental autonomy. Methods The literature was based methodology proposed by renowned British scholar, professor emerita computer science at Keele University Barbara Kitchenham, chosen for its flexibility effectiveness in obtaining publication. Thorough searches were carried out with search terms “neurotechnology”, “personal data”, “mental privacy”, “neuro-rights”, “neurotechnological interventions”, discrimination” both English Spanish sites, using engines like Google Scholar Redib as well databases including Scielo, Dialnet, Redalyc, Lilacs, Scopus, Medline, Pubmed. focus this is bibliometric data design non-experimental cross-sectional descriptive, content analysis PRISMA model. Results study emphasizes need establish clear principles protect rights promote responsible use neurotechnologies; number problems autonomy identified, such improper handling information, lack security guarantees, violation freedoms medical sphere. author shows adapt existing framework address arising from new neurotechnologies. It noted that broad will contribute human rights. Scientific novelty: an expanded understanding five neurorights within Universal Declaration Human Rights proposed; are viewed category aimed protecting integrity against misuse justifies adoption technocratic personal identity, free will, privacy, equal access bias. Practical significance: obtained relevant modern adapting normative acts solve emergence technologies, liability their violation. these key provision further development

Language: Английский

Implementation of artificial intelligence and machine learning-based methods in brain–computer interaction DOI Creative Commons
Katerina Barnova, Martina Mikolasova, Radana Kahánková

et al.

Computers in Biology and Medicine, Journal Year: 2023, Volume and Issue: 163, P. 107135 - 107135

Published: June 8, 2023

Brain–computer interfaces are used for direct two-way communication between the human brain and computer. Brain signals contain valuable information about mental state activity of examined subject. However, due to their non-stationarity susceptibility various types interference, processing, analysis interpretation challenging. For these reasons, research in field brain–computer is focused on implementation artificial intelligence, especially five main areas: calibration, noise suppression, communication, condition estimation, motor imagery. The use algorithms based intelligence machine learning has proven be very promising application domains, ability predict learn from previous experience. Therefore, within medical technologies can contribute more accurate subjects, alleviate consequences serious diseases or improve quality life disabled patients.

Language: Английский

Citations

26
A novelty towards neural signatures − Unveiling the inter-subject distance metric for EEG-based motor imagery DOI
Hajra Murtaza, Musharif Ahmed, Ghulam Murtaza

et al.

Biomedical Signal Processing and Control, Journal Year: 2025, Volume and Issue: 105, P. 107552 - 107552

Published: Feb. 6, 2025

Language: Английский

Citations

0
Improved performance of fNIRS-BCI by stacking of deep learning-derived frequency domain features DOI Creative Commons
Jamila Akhter, Hammad Nazeer, Noman Naseer

et al.

PLoS ONE, Journal Year: 2025, Volume and Issue: 20(4), P. e0314447 - e0314447

Published: April 17, 2025

The functional near-infrared spectroscopy-based brain-computer interface (fNIRS-BCI) systems recognize patterns in brain signals and generate control commands, thereby enabling individuals with motor disabilities to regain autonomy. In this study hand gripping data is acquired using fNIRS neuroimaging system, preprocessing performed nirsLAB features extraction deep learning (DL) Algorithms. For feature classification stack fft methods are proposed. Convolutional neural networks (CNN), long short-term memory (LSTM), bidirectional long-short-term (Bi-LSTM) employed extract features. method classifies these a model the enhances by applying fast Fourier transformation which followed model. proposed applied from twenty participants engaged two-class hand-gripping activity. performance of compared conventional CNN, LSTM, Bi-LSTM algorithms one another. yield 90.11% 87.00% accuracies respectively, significantly higher than those achieved CNN (85.16%), LSTM (79.46%), (81.88%) algorithms. results show that can be effectively used for two three-class problems fNIRS-BCI applications.

Language: Английский

Citations

0
Ethical and Safety Challenges of Implantable Brain-Computer Interface DOI Creative Commons
Ihab A. Satam, Róbert Szabolcsi

Interdisciplinary Description of Complex Systems, Journal Year: 2025, Volume and Issue: 23(2), P. 82 - 94

Published: Jan. 1, 2025

Language: Английский

Citations

0
ARiViT: attention-based residual-integrated vision transformer for noisy brain medical image classification DOI
Madiha Hameed,

Aneela Zameer,

Saddam Hussain Khan

et al.

The European Physical Journal Plus, Journal Year: 2024, Volume and Issue: 139(5)

Published: May 24, 2024

Language: Английский

Citations

3
Electroencephalography-Based Brain-Computer Interfaces in Rehabilitation: A Bibliometric Analysis (2013–2023) DOI Creative Commons

Ana Medina,

Manuel Bonilla,

Ingrid Daniela Rodríguez Giraldo

et al.

Sensors, Journal Year: 2024, Volume and Issue: 24(22), P. 7125 - 7125

Published: Nov. 6, 2024

EEG-based Brain-Computer Interfaces (BCIs) have gained significant attention in rehabilitation due to their non-invasive, accessible ability capture brain activity and restore neurological functions patients with conditions such as stroke spinal cord injuries. This study offers a comprehensive bibliometric analysis of global BCI research from 2013 2023. It focuses on primary review articles addressing technological innovations, effectiveness, system advancements clinical rehabilitation. Data were sourced databases like Web Science, tools (bibliometrix R) used analyze publication trends, geographic distribution, keyword co-occurrences, collaboration networks. The results reveal rapid increase EEG-BCI research, peaking 2022, focus motor sensory EEG remains the most commonly method, contributions Asia, Europe, North America. Additionally, there is growing interest applying BCIs mental health, well integrating artificial intelligence (AI), particularly machine learning, enhance accuracy adaptability. However, challenges remain, inefficiencies slow learning curves. These could be addressed by incorporating multi-modal approaches advanced neuroimaging technologies. Further needed validate applicability both cognitive rehabilitation, especially considering high prevalence cerebrovascular diseases. To advance field, expanding participation, underrepresented regions Latin America, essential. Improving efficiency through AI integration also critical. Ethical considerations, including data privacy, transparency, equitable access technologies, must prioritized ensure inclusive development use these technologies across diverse socioeconomic groups.

Language: Английский

Citations

3
Posthoc Interpretability of Neural Responses by Grouping Subject Motor Imagery Skills Using CNN-Based Connectivity DOI Creative Commons
Diego Collazos-Huertas, Andrés Marino Álvarez-Meza, David Cárdenas‐Peña

et al.

Sensors, Journal Year: 2023, Volume and Issue: 23(5), P. 2750 - 2750

Published: March 2, 2023

Motor Imagery (MI) refers to imagining the mental representation of motor movements without overt activity, enhancing physical action execution and neural plasticity with potential applications in medical professional fields like rehabilitation education. Currently, most promising approach for implementing MI paradigm is Brain-Computer Interface (BCI), which uses Electroencephalogram (EEG) sensors detect brain activity. However, MI-BCI control depends on a synergy between user skills EEG signal analysis. Thus, decoding responses recorded by scalp electrodes poses still challenging due substantial limitations, such as non-stationarity poor spatial resolution. Also, an estimated third people need more accurately perform tasks, leading underperforming systems. As strategy deal BCI-Inefficiency, this study identifies subjects performance at early stages BCI training assessing interpreting elicited across evaluated subject set. Using connectivity features extracted from class activation maps, we propose Convolutional Neural Network-based framework learning relevant information high-dimensional dynamical data distinguish tasks while preserving post-hoc interpretability responses. Two approaches inter/intra-subject variability data: (a) Extracting functional spatiotemporal maps through novel kernel-based cross-spectral distribution estimator, (b) Clustering according their achieved classifier accuracy, aiming find common discriminative patterns skills. According validation results obtained bi-class database, average accuracy enhancement 10% compared baseline EEGNet approach, reducing number “poor skill” 40% 20%. Overall, proposed method can be used help explain even deficient skills, who have high EEG-BCI performance.

Language: Английский

Citations

4
East Asian perspective of responsible research and innovation in neurotechnology DOI Creative Commons
Tamami Fukushi

IBRO Neuroscience Reports, Journal Year: 2024, Volume and Issue: 16, P. 582 - 597

Published: May 5, 2024

After more than half a century of research and development (R&D), Brain–computer interface (BCI)-based Neurotechnology continues to progress as one the leading technologies 2020 s worldwide. Various reports academic literature in Europe United States (U.S.) have outlined trends R&D neurotechnology consideration ethical issues, importance formulation principles, guidance industrial standards well relevant human resources has been discussed. However, limited number studies focused on R&D, dissemination neuroethics related foundation advancing discussion or resource Asian region. This study fills this gap understanding Eastern (China, Korea Japan) situation based participation activities develop guidance, for appropriate use neurotechnology, addition survey clinical registries' search investigation reflecting its social implication The current compared results with Europa U.S. discussed issues that need be addressed future significance potential corporate consortium initiatives Japan examples ethics governance Countries.

Language: Английский

Citations

1
Electrical stimulation-based paradigm to enhance lower limb motor imagery: initial validation in stroke patients DOI
Gui Zhang, Yuan Liu, Shiyin Qiu

et al.

Published: July 15, 2024

Lower limb motor dysfunction is a prevalent complication of stroke that significantly impacts patients' quality life. Current research indicates imagery-based brain-computer interface (BCI-MI) training can assist patients in enhancing function and reconstructing neural pathways. Nevertheless, 40% struggle with effective imagery (MI), leading to challenges applying lower MI clinical settings. Electrical stimulation (ES) has demonstrated the ability induce muscle contractions, generating kinesthetic illusion effectively guides subjects performing MI. However, existing study lacks clarity regarding effectiveness ES-MI paradigm improving patients. To address this gap, we recruited seven participate an experiment involving enhancement paradigm, aiming validate its performance The results revealed augmented activation cortex reactivated dormant areas, suggesting based on holds promise for remodeling effects Additionally, enhanced classification accuracy SVM(+1.17%), KNN(+0.93%), RF(+7.13%), LDA(+5.29%), EEGNet(+0.96%), indicating potential improvements efficiency human-robot interaction brain-controlled rehabilitation robots.

Language: Английский

Citations

1
Neurorights, Neurotechnologies and Personal Data: Review of the Challenges of Mental Autonomy DOI Creative Commons
Yan An Cornejo Montoya

Journal of Digital Technologies and Law, Journal Year: 2024, Volume and Issue: 2(3), P. 711 - 728

Published: Nov. 10, 2024

Objective : to present the results of a systematic review research on impact neurotechnology legal concepts and regulatory frameworks, addressing ethical social issues related protection individual rights, privacy mental autonomy. Methods The literature was based methodology proposed by renowned British scholar, professor emerita computer science at Keele University Barbara Kitchenham, chosen for its flexibility effectiveness in obtaining publication. Thorough searches were carried out with search terms “neurotechnology”, “personal data”, “mental privacy”, “neuro-rights”, “neurotechnological interventions”, discrimination” both English Spanish sites, using engines like Google Scholar Redib as well databases including Scielo, Dialnet, Redalyc, Lilacs, Scopus, Medline, Pubmed. focus this is bibliometric data design non-experimental cross-sectional descriptive, content analysis PRISMA model. Results study emphasizes need establish clear principles protect rights promote responsible use neurotechnologies; number problems autonomy identified, such improper handling information, lack security guarantees, violation freedoms medical sphere. author shows adapt existing framework address arising from new neurotechnologies. It noted that broad will contribute human rights. Scientific novelty: an expanded understanding five neurorights within Universal Declaration Human Rights proposed; are viewed category aimed protecting integrity against misuse justifies adoption technocratic personal identity, free will, privacy, equal access bias. Practical significance: obtained relevant modern adapting normative acts solve emergence technologies, liability their violation. these key provision further development

Language: Английский

Citations

0