Automatic control of reactive brain computer interfaces

Tufvesson, Pex; Heskebeck, Frida

doi:10.1016/j.ifacsc.2024.100251

Cited by 3 publications

(2 citation statements)

References 40 publications

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“…In this review study, we examine non-invasive methods that use scalp-mounted sensors to detect the electrical potentials generated by the brain (EEG). Non-invasive wearable devices such as headbands can measure brainwaves with the use of electroencephalography sensors [25]. These neurofeedback technologies are user-friendly, portable, and low-cost.…”

Section: The Potential Of Wearables Biofeedback and Non-invasive Neur...mentioning

confidence: 99%

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Artificial Intelligence, Immersive Technologies, and Neurotechnologies in Breathing Interventions for Mental and Emotional Health: A Systematic Review

Mitsea,

Drigas,

Skianis

2024

Electronics

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Breathing is one of the most vital functions for being mentally and emotionally healthy. A growing number of studies confirm that breathing, although unconscious, can be under voluntary control. However, it requires systematic practice to acquire relevant experience and skillfulness to consciously utilize breathing as a tool for self-regulation. After the COVID-19 pandemic, a global discussion has begun about the potential role of emerging technologies in breath-control interventions. Emerging technologies refer to a wide range of advanced technologies that have already entered the race for mental health training. Artificial intelligence, immersive technologies, biofeedback, non-invasive neurofeedback, and other wearable devices provide new, but yet underexplored, opportunities in breathing training. Thus, the current systematic review examines the synergy between emerging technologies and breathing techniques for improving mental and emotional health through the lens of skills development. The Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) methodology is utilized to respond to the objectives and research questions. The potential benefits, possible risks, ethical concerns, future directions, and implications are also discussed. The results indicated that digitally assisted breathing can improve various aspects of mental health (i.e., attentional control, emotional regulation, mental flexibility, stress management, and self-regulation). A significant finding of this review indicated that the blending of different technologies may maximize training outcomes. Thus, future research should focus on the proper design and evaluation of different digital designs in breathing training to improve health in different populations. This study aspires to provide positive feedback in the discussion about the role of digital technologies in assisting mental and emotional health-promoting interventions among populations with different needs (i.e., employees, students, and people with disabilities).

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Section: The Potential Of Wearables Biofeedback and Non-invasive Neur...mentioning

confidence: 99%

“…Multimodal biofeedback is commonly used to help users gain voluntary control over their physiological processes [24]. Non-invasive wearable devices such as headbands can measure brainwaves with the use of electroencephalography (EEG) sensors [25]. These neurofeedback technologies are user-friendly, portable, and low-cost.…”

Section: Introductionmentioning

confidence: 99%

Artificial Intelligence, Immersive Technologies, and Neurotechnologies in Breathing Interventions for Mental and Emotional Health: A Systematic Review

Mitsea,

Drigas,

Skianis

2024

Electronics

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Automatic Control of a Wheelchair Using a Brain Computer Interface and Real-Time Decision-Making

Heskebeck,

Tufvesson

2024

2024 European Control Conference (ECC)

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Improving the Performance of Electrotactile Brain–Computer Interface Using Machine Learning Methods on Multi-Channel Features of Somatosensory Event-Related Potentials

Novičić,

Djordjević,

Miler-Jerković

et al. 2024

Sensors

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Traditional tactile brain–computer interfaces (BCIs), particularly those based on steady-state somatosensory–evoked potentials, face challenges such as lower accuracy, reduced bit rates, and the need for spatially distant stimulation points. In contrast, using transient electrical stimuli offers a promising alternative for generating tactile BCI control signals: somatosensory event-related potentials (sERPs). This study aimed to optimize the performance of a novel electrotactile BCI by employing advanced feature extraction and machine learning techniques on sERP signals for the classification of users’ selective tactile attention. The experimental protocol involved ten healthy subjects performing a tactile attention task, with EEG signals recorded from five EEG channels over the sensory–motor cortex. We employed sequential forward selection (SFS) of features from temporal sERP waveforms of all EEG channels. We systematically tested classification performance using machine learning algorithms, including logistic regression, k-nearest neighbors, support vector machines, random forests, and artificial neural networks. We explored the effects of the number of stimuli required to obtain sERP features for classification and their influence on accuracy and information transfer rate. Our approach indicated significant improvements in classification accuracy compared to previous studies. We demonstrated that the number of stimuli for sERP generation can be reduced while increasing the information transfer rate without a statistically significant decrease in classification accuracy. In the case of the support vector machine classifier, we achieved a mean accuracy over 90% for 10 electrical stimuli, while for 6 stimuli, the accuracy decreased by less than 7%, and the information transfer rate increased by 60%. This research advances methods for tactile BCI control based on event-related potentials. This work is significant since tactile stimulation is an understudied modality for BCI control, and electrically induced sERPs are the least studied control signals in reactive BCIs. Exploring and optimizing the parameters of sERP elicitation, as well as feature extraction and classification methods, is crucial for addressing the accuracy versus speed trade-off in various assistive BCI applications where the tactile modality may have added value.

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Automatic control of reactive brain computer interfaces

Cited by 3 publications

References 40 publications

Artificial Intelligence, Immersive Technologies, and Neurotechnologies in Breathing Interventions for Mental and Emotional Health: A Systematic Review

Artificial Intelligence, Immersive Technologies, and Neurotechnologies in Breathing Interventions for Mental and Emotional Health: A Systematic Review

Automatic Control of a Wheelchair Using a Brain Computer Interface and Real-Time Decision-Making

Improving the Performance of Electrotactile Brain–Computer Interface Using Machine Learning Methods on Multi-Channel Features of Somatosensory Event-Related Potentials

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