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![Figure 3. Sample net of electrodes placed on the cortex surface [3,34].](publication/348163850/figure/fig3/AS:976563977859073@1609842404460/Sample-net-of-electrodes-placed-on-the-cortex-surface-3-34_Q320.jpg)
![Figure 4. Hans Berger in his lab [37].](publication/348163850/figure/fig4/AS:976563977863168@1609842404568/Hans-Berger-in-his-lab-37_Q320.jpg)
![Figure 5. BCI-sensors types [148,149].](publication/348163850/figure/fig5/AS:976563977846786@1609842404699/BCI-sensors-types-148-149_Q320.jpg)
Over the last few decades, the Brain-Computer Interfaces have been gradually making their way to the epicenter of scientific interest. Many scientists from all around the world have contributed to the state of the art in this scientific domain by developing numerous tools and methods for brain signal acquisition and processing. Such a spectacular p...
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Context 1
... Figure 2 three different methods used for the recording of the electrical activity of brain, including one non-invasive (EEG) and two invasive (ECoG and intracortical recordings) are illustrated [3,27,31]. The ECoG recordings provide stronger and better-quality signals than the EEG data, mainly because of their following [22,30]: ...
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Citations
... Solutions to restore control of the limbs and trunk include rehabilitative training, therapies, cellular (transplantation of peripheral nerve cells, Schwann cells), and molecular approaches (axonal conduction enhancement). 1,2 One such approach that is both novel and practical, as well as cost-efficient which requires minimum invasive surgical intervention, is the braincomputer interface (BCI). BCI establishes a system of communication between computers and brain functionality. ...
Individuals who are suffering from the most severe of motor disabilities can improve their quality of life by controlling and directing mechanical and electronic devices. As for Spinal Cord Injured (SCI) patients', attempted hand movements can be classified using electroencephalography (EEG). The research aims to develop a hybrid CNN‐LSTM (Convolutional Neural Network—Long Short Term Memory) architecture for multichannel EEG signal classification. It is a challenging task to classify real‐world multichannel EEG data from SCI patients. The proposed research preprocessed the EEG data to improve the signal‐to‐noise ratio and arranged for them to extract additional information from the data. The preprocessing step includes filtering, downsampling, and artifact removal, while the postprocessing step includes time‐frequency representation and spatial information encoding. A hybrid CNN‐LSTM is used for feature extraction and classification. The proposed method has been implemented on a dataset consisting of 5 different classes of attempted hand movements from 10 SCI patients. The average classification accuracy of 92.36% is achieved for 5‐class classification. To check the global validity of the proposed network, the BCI competition IV data is classified by the proposed method and has found 92.70% overall accuracy.
... Both electroencephalography (EEG) or functional Near-Infrared Spectroscopy (fNIRS) stand out as the predominant non-invasive methods for acquiring brain data [7]. EEG directly captures the brain's bio-electrical activity by recording electrical fluctuations via electrodes placed on the scalp [7]. ...
... Both electroencephalography (EEG) or functional Near-Infrared Spectroscopy (fNIRS) stand out as the predominant non-invasive methods for acquiring brain data [7]. EEG directly captures the brain's bio-electrical activity by recording electrical fluctuations via electrodes placed on the scalp [7]. In contrast, fNIRS relies on optical techniques to detect changes in hemodynamics [21], typically induced by cortical responses during motor, cognitive, and perceptual functions of the brain. ...
Present Brain-Computer Interfacing (BCI) technology allows inference and detection of cognitive and affective states, but fairly little has been done to study scenarios in which such information can facilitate new applications that rely on modeling human cognition. One state that can be quantified from various physiological signals is attention. Estimates of human attention can be used to reveal preferences and novel dimensions of user experience. Previous approaches have tackled these incredibly challenging tasks using a variety of behavioral signals, from dwell-time to click-through data, and computational models of visual correspondence to these behavioral signals. However, behavioral signals are only rough estimations of the real underlying attention and affective preferences of the users. Indeed, users may attend to some content simply because it is salient, but not because it is really interesting, or simply because it is outrageous. With this paper, we put forward a research agenda and example work using BCI to infer users' preferences, their attentional correlates towards visual content, and their associations with affective experience. Subsequently, we link these to relevant applications, such as information retrieval, personalized steering of generative models, and crowdsourcing population estimates of affective experiences.
... For this study, BCI refers to an information technology that is placed on the outside of the brain that enables humans to interact with technology without any body movement, using only electrical signals generated in the brain to record activity [7]. BCIs have primarily been researched to provide communication abilities to disabled or "locked-in" patients [8]. Simultaneously, service researchers debate impacts of human-enhancement technologies, such as BCI, on customer experiences [5]. ...
... These devices can be worn inconspicuously as headphones or glasses. Most existing research on BCI has primarily focused on extracting features from brain waves or developing medical applications to assist users with brain injuries or locked-in states to communicate or control robotic extensions [8]. Despite these efforts, there has been a lack of research on the acceptance of BCI and the impact on guest experience [2,3,11]. ...
This paper explores the emerging role of Brain-Computer Interfaces (BCI) in the hospitality industry. BCI technology allows users to control devices with their thoughts, potentially transforming guest experiences. The study investigates how guests perceive BCI-enhanced experiences compared to traditional ones. Drawing from service and human-computer interaction literature, the paper conducts a quasi-field pre-study, where participants interact with a BCI-equipped waitress. Surprisingly, participants perceived the BCI-equipped waitress as superior and warmer, resulting in an improved service experience.
The research contributes in two ways: it advances understanding of how people perceive BCI-augmented interactions in hospitality and explores the use of BCIs in addressing service failures, improving efficiency in handling customer complaints. The paper outlines plans for larger-scale field studies and online experiments across different hospitality contexts. This research offers insights into the evolving landscape of human-computer interaction in hospitality, with practical implications for the industry’s future.
... This idea has been realized over time with the development of the elemental technology fields related to BCI (for example computer science, information and communication engineering, artificial intelligence, computational neuroscience, etc.) and with the cooperation of the clinical medicines. In the early 2000 s, BCI with unit recording reached the stage of clinical research (Hochberg, et al., 2006), then through the 2010 s, applied research using BCI and research on improving and upgrading elemental technologies such as sensing, signal processing and decoding became popular (Alharbi, 2023;Kawala-Sterniuk et al. 2021;Maiseli et al. 2023;Saha et al., 2021). Around the same time, the U.S. Food and Drug Administration Center for Devices and Radiological Health (FDA-CDHR) initiated a study to prepare for an approval review of BCI based on its practical feasibility in clinical practice (Bowsher et al., 2016); the FDA-CDHR issued "leap-frog" guidance in May 2021 with recommendations for the design of nonclinical and clinical testing using BCI devices for patients with paralysis or amputation (Department of Health and Human Services et al., 2021), also see https://www.fda.gov ...
After more than half a century of research and development (R&D), Brain–computer interface (BCI)-based Neurotechnology continues to progress as one of the leading technologies of the 2020 s worldwide. Various reports and academic literature in Europe and the United States (U.S.) have outlined the trends in the R&D of neurotechnology and the consideration of ethical issues, and the importance of the formulation of ethical principles, guidance and industrial standards as well as the development of relevant human resources has been discussed. However, limited number studies have focused on neurotechnology R&D, the dissemination of neuroethics related to the academic foundation advancing the discussion on ethical principles, guidance and standards or human resource development in the Asian region. This study fills in this gap in understanding of Eastern Asian (China, Korea and Japan) situation based on the participation in activities to develop ethical principles, guidance, and industrial standards for appropriate use of neurotechnology, in addition to literature survey and clinical registries’ search investigation reflecting the trends in neurotechnology R&D as well as its social implication in Asian region. The current study compared the results with the situation in Europa and the U.S. and discussed issues that need to be addressed in the future and discussed the significance and potential of corporate consortium initiatives in Japan and examples of ethics and governance activities in Asian Countries.
... Brain-Computer Interfaces (BCIs) are used in these robot-assisted rehabilitation systems for patients who cannot move their limbs voluntarily (Robinson et al. 2021). BCIs measure electrical activities in the brain and convert them into commands by using signal processing and pattern analysis methods (Kawala-Sterniuk et al. 2021). One of the most used techniques to trigger and emerge the electrical activities in the patient's brain is the Motor Imagery (MI) technique. ...
This study investigates the influence of immersive virtual reality environments and gamification on the classification of imaginary motor (MI) signals and the associated increase in energy in the motor cortex region for neurorehabilitation purposes. Two immersive virtual environments, indoor and outdoor, were selected, each with gamified and non-gamified scenarios. Event-Related Desynchronization (ERD) data underwent analyses to determine if there were significant differences in ERD levels between distinct age groups and whether Fully Immersive Virtual Reality (FIVR) environments induced notable energy increases.
The initial analysis found no significant energy changes between age groups under constant environmental conditions. In the second analysis, FIVR environments did not lead to a statistically significant increase in cortical energy for the 21–24 age group (Group I). However, a notable difference in cortical energy increase was identified between gamified and non-gamified environments within the 32–43 age group (Group II).
The study also explored the impact of environmental factors on MI signal classification using four deep learning algorithms. The Recurrent Neural Network (RNN) classifier exhibited the highest performance, with an average accuracy of 86.83%. Signals recorded indoors showed higher average classification performance, with a significant difference observed among age groups. Group I participants performed better in non-gamified environments (88.8%), while Group II achieved high performance indoors, especially in the gamified scenario (93.6%). Overall, the research underscores the potential of immersive virtual environments and gamification in enhancing MI signal classification and cortical energy increase, with age and environmental factors influencing the outcomes.
... Non-invasive brain-computer interfaces (BCIs) serve as a link between the human brain and external devices, such as computers and robots (Hramov et al., 2021;Saha et al., 2021;Kawala-Sterniuk et al., 2021). They enable the conversion of brain activity into control commands by analyzing electroencephalogram (EEG) data. ...
This manuscript presents a novel approach for decoding pre-movement patterns from brain signals using a two-stage-training temporal-spectral neural network (TTSNet). The TTSNet employs a combination of filter bank task-related component analysis (FBTRCA) and convolutional neural network (CNN) techniques to enhance the classification of single-upper limb movements in non-invasive brain-computer interfaces (BCIs). In our previous work, we introduced the FBTRCA method which utilized filter banks and spatial filters to handle spectral and spatial information, respectively. However, we observed limitations in the temporal decoding phase, where correlation features failed to effectively utilize temporal information because of misaligned onset and noisy spikes. To address this issue, our proposed method focuses on analyzing multi-channel signals in the temporal-spectral domain. The TTSNet first divides the signals into various filter banks, employing task-related component analysis to reduce dimensionality and eliminate noise, respectively. Subsequently, a CNN is employed to optimize the temporal characteristics of the signals and extract class-related features. Finally, the class-related features from all filter banks are concatenated and classified using the fully connected layer. To evaluate the effectiveness of our proposed method, we conducted experiments on two publicly available datasets. In binary classification tasks, the TTSNet achieved an improved accuracy of 0.7707 ± 0.1168, surpassing the performance of EEGNet (accuracy: 0.7340 ± 0.1246) and FBTRCA (accuracy: 0.7487 ± 0.1250). In multi-class tasks, TTSNet achieved an accuracy of 0.4588 ± 0.0724, exhibiting a 4.27% and 3.95% accuracy increase over EEGNet and FBTRCA, respectively. The findings of this study suggest that the proposed TTSNet method holds promise for detecting limb movements and assisting in the rehabilitation of stroke patients. The classification of single-side limb movements is expected to facilitate the interaction between patients and external environment by increasing the number of control commands in BCIs.
... Despite the importance of these innate needs, there have been few neuroscientific studies of the neural signals associated with inner motivational states in people who are unable to communicate verbally. For example, in Brain Computer Interface (BCI) studies, the recording and classification of electrical potentials is used to infer the mental content of patients with locked-in syndrome (LIS, 3). Patients who are conscious and can generate motor commands or readiness potentials (4,5), or can make voluntary decisions by generating P300 components (6), can communicate by controlling cursors, robots, prostheses, speller systems (7), or objects with their volitional signals. ...
Introduction
The capacity to understand the others’ emotional states, particularly if negative (e.g. sadness or fear), underpins the empathic and social brain. Patients who cannot express their emotional states experience social isolation and loneliness, exacerbating distress. We investigated the feasibility of detecting non-invasive scalp-recorded electrophysiological signals that correspond to recalled emotional states of sadness, fear, and joy for potential classification.
Methods
The neural activation patterns of 20 healthy and right-handed participants were studied using an electrophysiological technique. Analyses were focused on the N400 component of Event-related potentials (ERPs) recorded during silent recall of subjective emotional states; Standardized weighted Low-resolution Electro-magnetic Tomography (swLORETA) was employed for source reconstruction. The study classified individual patterns of brain activation linked to the recollection of three distinct emotional states into seven regions of interest (ROIs).
Results
Statistical analysis (ANOVA) of the individual magnitude values revealed the existence of a common emotional circuit, as well as distinct brain areas that were specifically active during recalled sad, happy and fearful states. In particular, the right temporal and left superior frontal areas were more active for sadness, the left limbic region for fear, and the right orbitofrontal cortex for happy affective states.
Discussion
In conclusion, this study successfully demonstrated the feasibility of detecting scalp-recorded electrophysiological signals corresponding to internal and subjective affective states. These findings contribute to our understanding of the emotional brain, and have potential applications for future BCI classification and identification of emotional states in LIS patients who may be unable to express their emotions, thus helping to alleviate social isolation and sense of loneliness.
... These interfaces harness the power of electroencephalography (EEG) devices to monitor and interpret mental activity, translating specific cognitive signals into actionable instructions for device control [1]. Among the various facets of BCI research, the utilization of EEG signals holds immense promise, as it paves the way for effective integration with the Internet of Things (IoT) and the creation of smart and accessible home environments [2]. The integration of BCIs and IoT technology has opened new avenues for enhancing the quality of life, convenience, and accessibility in daily living spaces. ...
Through a Bluetooth connection between the Muse 2 device and the meditation app, leveraging IoT capabilities. The methodology encompasses data collection, preprocessing, feature extraction, and model training, all while utilizing Internet of Things (IoT) functionalities. The Muse 2 device records EEG data from multiple electrodes, which is then processed and analyzed within a mobile meditation platform. Preprocessing steps involve eliminating redundant columns, handling missing data, normalizing, and filtering, making use of IoT-enabled techniques. Feature extraction is carried out on EEG signals, utilizing statistical measures such as mean, standard deviation, and entropy. Three different models, including Support Vector Machine (SVM), Random Forest, and Multi-Layer Perceptron (MLP), are trained using the preprocessed data, incorporating Internet of Things (IoT) based methodologies. Model performance is assessed using metrics like accuracy, precision, recall, and F1-score, highlighting the effectiveness of IoT-driven techniques. Notably, the MLP and Random Forest models demonstrate remarkable accuracy and precision, underlining the potential of this IoT-integrated approach. Specifically, the three models achieved high accuracies, with Random Forest leading at 0.999, followed by SVM at 0.959 and MLP at 0.99. This study not only contributes to the field of brain-computer interfaces and assistive technologies but also showcases a viable method to seamlessly integrate the Muse 2 device into meditation practices, promoting self-awareness and mindfulness with the added power of IoT technology.
... The actual efficacy of several BCI products available in the market is challenging to quantify (Kawala-Sterniuk et al., 2021), and some of these products have not demonstrated any efficacy. The post-purchase usage of BCI products is not promising, with minimal usage leading to little or no improvement in users' quality of life. ...
Brain-computer interface (BCI) is a revolutionizing human-computer interaction, which has potential applications for specific individuals or groups in specific scenarios. Extensive research has been conducted on the principles and implementation methods of BCI, and efforts are currently being made to bridge the gap from research to real-world applications. However, there are inaccurate or erroneous conceptions about BCI among some members of the public, and certain media outlets, as well as some BCI researchers, developers, manufacturers, and regulators, propagate misleading or overhyped claims about BCI technology. Therefore, this article summarizes the several misconceptions and misleading propaganda about BCI, including BCI being capable of “mind-controlled,” “controlling brain,” “mind reading,” and the ability to “download” or “upload” information from or to the brain using BCI, among others. Finally, the limitations (shortcomings) and limits (boundaries) of BCI, as well as the necessity of conducting research aimed at countering BCI systems are discussed, and several suggestions are offered to reduce misconceptions and misleading claims about BCI.
... RAIN-COMPUTER interface (BCI) is an advanced technology that establishes a direct connection between the human brain and external devices [1], [2], [3]. Among various BCI paradigms, the steady-state visual evoked potential (SSVEP) [4] is widely utilized due to its advantages of low training demand and high information transfer rate (ITR). ...
In steady-state visual evoked potential (SSVEP)-based brain-computer interface (BCI) systems, traditional flickering stimulation patterns face challenges in achieving a trade-off in both BCI performance and visual comfort across various frequency bands. To investigate the optimal stimulation paradigms with high performance and high comfort for each frequency band, this study systematically compared the characteristics of SSVEP and user experience of different stimulation paradigms with a wide stimulation frequency range of 1-60 Hz. The findings suggest that, for a better balance between system performance and user experience, ON and OFF grid stimuli with a Weber contrast of 50% can be utilized as alternatives to traditional flickering stimulation paradigms in the frequency band of 1-25 Hz. In the 25-35 Hz range, uniform flicker stimuli with the same 50% contrast are more suitable. In the higher frequency band, traditional uniform flicker stimuli with a high 300% contrast are preferred. These results are significant for developing high performance and user-friendly SSVEP-based BCI systems.
