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Recent advances in electrodes for noninvasive recording of electroencephalograms expand opportunities collecting such data for diagnosis of neurological disorders and brain–computer interfaces. Existing technologies, however, cannot be used effectively in continuous, uninterrupted modes for more than a few days due to irritation and irreversible degradation in the electrical and mechanical properties of the skin interface. Here we introduce a soft, foldable collection of electrodes in open, fractal mesh geometries that can mount directly and chronically on the complex surface topology of the auricle and the mastoid, to provide high-fidelity and long-term capture of electroencephalograms in ways that avoid any significant thermal, electrical, or mechanical loading of the skin. Experimental and computational studies establish the fundamental aspects of the bending and stretching mechanics that enable this type of intimate integration on the highly irregular and textured surfaces of the auricle. Cell level tests and thermal imaging studies establish the biocompatibility and wearability of such systems, with examples of high-quality measurements over periods of 2 wk with devices that remain mounted throughout daily activities including vigorous exercise, swimming, sleeping, and bathing. Demonstrations include a text speller with a steady-state visually evoked potential-based brain–computer interface and elicitation of an event-related potential (P300 wave). Significance Conventional electroencephalogram (EEG) recording systems, particularly the hardware components that form the physical interfaces to the head, have inherent drawbacks that limit the widespread use of continuous EEG measurements for medical diagnostics, sleep monitoring, and cognitive control. Here we introduce soft electronic constructs designed to intimately conform to the complex surface topology of the auricle and the mastoid, to provide long-term, high-fidelity recording of EEG data. Systematic studies reveal key aspects of the extreme levels of bending and stretching that are involved in mounting on these surfaces. Examples in persistent brain–computer interfaces, including text spellers with steady-state visually evoked potentials and event-related potentials, with viable operation over periods of weeks demonstrate important advances over alternative brain–computer interface technologies.

Groping around a familiar room in the dark, relearning to read after a brain injury, navigating a virtual landscape through an avatar: all are expressions of vicariance--when the brain substitutes one process or function for another. Alain Berthoz shows that this capacity allows humans to think creatively in an increasingly complex world.-- Provided by publisher.

Artificial intelligence and brain-computer interfaces must respect and preserve people's privacy, identity, agency and equality, say Rafael Yuste, Sara Goering and colleagues.

The use of imagined speech with electroencephalographic (EEG) signals is a promising field of brain-computer interfaces (BCI) that seeks communication between areas of the cerebral cortex related to language and devices or machines. However, the complexity of this brain process makes the analysis and classification of this type of signals a relevant topic of research. The goals of this study were: to develop a new algorithm based on Deep Learning (DL), referred to as CNNeeg1-1, to recognize EEG signals in imagined vowel tasks; to create an imagined speech database with 50 subjects specialized in imagined vowels from the Spanish language (/a/,/e/,/i/,/o/,/u/); and to contrast the performance of the CNNeeg1-1 algorithm with the DL Shallow CNN and EEGNet benchmark algorithms using an open access database (BD1) and the newly developed database (BD2). In this study, a mixed variance analysis of variance was conducted to assess the intra-subject and inter-subject training of the proposed algorithms. The results show that for intra-subject training analysis, the best performance among the Shallow CNN, EEGNet, and CNNeeg1-1 methods in classifying imagined vowels (/a/,/e/,/i/,/o/,/u/) was exhibited by CNNeeg1-1, with an accuracy of 65.62% for BD1 database and 85.66% for BD2 database.

Background Brain-Computer Interface (BCI) is a set of technologies that are of increasing interest to researchers. BCI has been proposed as assistive technology for individuals who are non-communicative or paralyzed, such as those with amyotrophic lateral sclerosis or spinal cord injury. The technology has also been suggested for enhancement and entertainment uses, and there are companies currently marketing BCI devices for those purposes (e.g., gaming) as well as health-related purposes (e.g., communication). The unprecedented direct connection created by BCI between human brains and computer hardware raises various ethical, social, and legal challenges that merit further examination and discussion. Methods To identify and characterize the key issues associated with BCI use, we performed a scoping review of biomedical ethics literature, analyzing the ethics concerns cited across multiple disciplines, including philosophy and medicine. Results Based on this investigation, we report that BCI research and its potential translation to therapeutic intervention generate significant ethical, legal, and social concerns, notably with regards to personhood, stigma, autonomy, privacy, research ethics, safety, responsibility, and justice. Our review of the literature determined, furthermore, that while these issues have been enumerated extensively, few concrete recommendations have been expressed. Conclusions We conclude that future research should focus on remedying a lack of practical solutions to the ethical challenges of BCI, alongside the collection of empirical data on the perspectives of the public, BCI users, and BCI researchers.

Brain–computer interfaces (BCIs) are a new technology that subverts traditional human–computer interaction, where the control signal source comes directly from the user’s brain. When a general BCI is used for practical applications, it is difficult for it to meet the needs of different individuals because of the differences among individual users in physiological and mental states, sensations, perceptions, imageries, cognitive thinking activities, and brain structures and functions. For this reason, it is necessary to customize personalized BCIs for specific users. So far, few studies have elaborated on the key scientific and technical issues involved in personalized BCIs. In this study, we will focus on personalized BCIs, give the definition of personalized BCIs, and detail their design, development, evaluation methods and applications. Finally, the challenges and future directions of personalized BCIs are discussed. It is expected that this study will provide some useful ideas for innovative studies and practical applications of personalized BCIs.

Brain‐computer interfaces (BCIs) can be used to control assistive devices by patients with neurological disorders like amyotrophic lateral sclerosis (ALS) that limit speech and movement. For assistive control, it is desirable for BCI systems to be accurate and reliable, preferably with minimal setup time. In this study, a participant with severe dysarthria due to ALS operates computer applications with six intuitive speech commands via a chronic electrocorticographic (ECoG) implant over the ventral sensorimotor cortex. Speech commands are accurately detected and decoded (median accuracy: 90.59%) throughout a 3‐month study period without model retraining or recalibration. Use of the BCI does not require exogenous timing cues, enabling the participant to issue self‐paced commands at will. These results demonstrate that a chronically implanted ECoG‐based speech BCI can reliably control assistive devices over long time periods with only initial model training and calibration, supporting the feasibility of unassisted home use. A speech brain‐computer interface enables a clinical‐trial participant with amyotrophic lateral sclerosis to control a communication board and home devices without model retraining or baseline calibration. Six intuitive speech commands are accurately detected and decoded (median accuracy: 90.59%) from a chronic electrocorticographic (ECoG) implant over the ventral sensorimotor cortex throughout a 3‐month study period.

There is a lack of multi-session P300 datasets for Brain-Computer Interfaces (BCI). Publicly available datasets are usually limited by small number of participants with few BCI sessions. In this sense, the lack of large, comprehensive datasets with various individuals and multiple sessions has limited advances in the development of more effective data processing and analysis methods for BCI systems. This is particularly evident to explore the feasibility of deep learning methods that require large datasets. Here we present the BCIAUT-P300 dataset, containing 15 autism spectrum disorder individuals undergoing 7 sessions of P300-based BCI joint-attention training, for a total of 105 sessions. The dataset was used for the 2019 IFMBE Scientific Challenge organized during MEDICON 2019 where, in two phases, teams from all over the world tried to achieve the best possible object-detection accuracy based on the P300 signals. This paper presents the characteristics of the dataset and the approaches followed by the 9 finalist teams during the competition. The winner obtained an average accuracy of 92.3% with a convolutional neural network based on EEGNet. The dataset is now publicly released and stands as a benchmark for future P300-based BCI algorithms based on multiple session data.

Brain–robot interfaces (BRI) are studied as novel interventions to facilitate functional restoration in patients with severe and persistent motor deficits following stroke. They bridge the impaired connection in the sensorimotor loop by providing brain-state dependent proprioceptive feedback with orthotic devices attached to the hand or arm of the patients. The underlying neurophysiology of this BRI neuromodulation is still largely unknown. We investigated changes of corticospinal excitability with transcranial magnetic stimulation in thirteen right-handed healthy subjects who performed 40min of kinesthetic motor imagery receiving proprioceptive feedback with a robotic orthosis attached to the left hand contingent to event-related desynchronization of the right sensorimotor cortex in the β-band (16–22Hz). Neural correlates of this BRI intervention were probed by acquiring the stimulus–response curve (SRC) of both motor evoked potential (MEP) peak-to-peak amplitudes and areas under the curve. In addition, a motor mapping was obtained. The specificity of the effects was studied by comparing two neighboring hand muscles, one BRI-trained and one control muscle. Robust changes of MEP amplitude but not MEP area occurred following the BRI intervention, but only in the BRI-trained muscle. The steep part of the SRC showed an MEP increase, while the plateau of the SRC showed an MEP decrease. MEP mapping revealed a distributed pattern with a decrease of excitability in the hand area of the primary motor cortex, which controlled the BRI, but an increase of excitability in the surrounding somatosensory and premotor cortex. In conclusion, the BRI intervention induced a complex pattern of modulated corticospinal excitability, which may boost subsequent motor learning during physiotherapy. Brain–robot interface feedback of motor-imagery related sensorimotor β-band desynchronization induced robust and muscle-specific changes of corticospinal excitability with opposing effects on the trained finger extension muscle with decreases (blue/turquoise) and increases (red/yellow) of excitability in the TMS motor map for the primary motor cortex and the surrounding somatosensory and premotor cortex, respectively. The green dot represents the ‘hot spot’, where the MEP stimulus response curve was acquired and resulted in opposing effects for the steep part and the plateau of the TMS stimulus–response curve with increases and decreases of excitability, respectively. The neurophysiological effects where related to modulated synchronization of the stimulated neuronal population and not to the recruitment of additional neurons. The training induced changes of β-band desynchronization in the somatosensory cortex of individual participants correlated with increased corticospinal excitability. [Display omitted] •BRI feedback of motor-imagery related sensorimotor β-band ERD induced robust and muscle-specific changes of corticospinal excitability.•The steep part and the plateau of the TMS stimulus–response curve revealed increases and decreases of excitability, respectively.•M1 and the surrounding somatosensory/premotor cortex revealed opposing effects with decreases and increases of excitability, respectively.•These effects where related to modulated synchronization and not to the recruitment of additional neurons.•The training induced changes of β-band ERD of individual participants correlated with increased corticospinal excitability.