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Objective Brain–computer interfaces (BCI) tested in patients often are gaze-dependent, while these intended users could possibly lose the ability to focus their gaze. Therefore, a visual and a tactile gaze-independent spelling system were investigated. Methods Five patients with amyotrophic lateral sclerosis (ALS) tested a visual Hex-o-Spell and a tactile speller. Six healthy participants were also included, mainly to evaluate the tactile stimulators. Results A significant attentional modulation was seen in the P300 for the Hex-o-Spell and in the N2 for the tactile speller. Average on-line classification performance for selecting a step in the speller was above chance level (17%) for both spellers. However, average performance was higher for the Hex-o-Spell (88% and 85% for healthy participants and patients, respectively) than for the tactile speller (56% and 53%, respectively). Likewise, bitrates were higher for the Hex-o-Spell compared with the tactile speller, and in the subjective usability a preference for the Hex-o-Spell was found. Conclusions The Hex-o-Spell outperformed the tactile speller in classification performance, bit rate and subjective usability. Significance This is the first study showing the possible use of tactile and visual gaze-independent BCI spelling systems by ALS patients with mild to moderate disabilities. [ABSTRACT FROM AUTHOR]

Abstract: Objective: An auditory rhythm can be perceived as a sequence of accented (loud) and non-accented (soft) beats or it can be imagined. Subjective rhythmization refers to the induction of accenting patterns during the presentation of identical auditory pulses at an isochronous rate. It can be an automatic process, but it can also be voluntarily controlled. We investigated whether imagined accents can be decoded from brain signals on a single-trial basis, and if there is information shared between perception and imagery in the contrast of accents and non-accents. Methods: Ten subjects perceived and imagined three different metric patterns (two-, three-, and four-beat) superimposed on a steady metronome while electroencephalography (EEG) measurements were made. Shared information between perception and imagery EEG is investigated by means of principal component analysis and by means of single-trial classification. Results: Classification of accented from non-accented beats was possible with an average accuracy of 70% for perception and 61% for imagery data. Cross-condition classification yielded significant performance above chance level for a classifier trained on perception and tested on imagery data (up to 66%), and vice versa (up to 60%). Conclusions: Results show that detection of imagined accents is possible and reveal similarity in brain signatures relevant to distinction of accents from non-accents in perception and imagery. Significance: Our results support the idea of shared mechanisms in perception and imagery for auditory processing. This is relevant for a number of clinical settings, most notably by elucidating the basic mechanisms of rhythmic auditory cuing paradigms, e.g. as used in motor rehabilitation or therapy for Parkinson’s disease. As a novel Brain–Computer Interface (BCI) paradigm, our results imply a reduction of the necessary BCI training in healthy subjects and in patients. [Copyright &y& Elsevier]

The present study aims to connect the psychophysical research on the human visual perception of flicker with the neurophysiological research on steady-state visual evoked potentials (SSVEPs) in the context of their application needs and current technological developments. In four experiments, we investigated whether a temporal contrast sensitivity model could be established based on the electrophysiological responses to repetitive visual stimulation and, if so, how this model compares to the psychophysical models of flicker visibility. We used data from 62 observers viewing periodic flicker at a range of frequencies and modulation depths sampled around the perceptual visibility thresholds. The resulting temporal contrast sensitivity curve (TCSC) was similar in shape to its psychophysical counterpart, confirming that the human visual system is most sensitive to repetitive visual stimulation at frequencies between 10 and 20 Hz. The electrophysiological TCSC, however, was below the psychophysical TCSC measured in our experiments for lower frequencies (1-50 Hz), crossed it when the frequency was 50 Hz, and stayed above while decreasing at a slower rate for frequencies in the gamma range (40-60 Hz). This finding provides evidence that SSVEPs could be measured even without the conscious perception of flicker, particularly at frequencies above 50 Hz. The cortical and perceptual mechanisms that apply at higher temporal frequencies, however, do not seem to directly translate to lower frequencies. The presence of harmonics, which show better response for many frequencies, suggests non-linear processing in the visual system. These findings are important for the potential applications of SSVEPs in studying, assisting, or augmenting human cognitive and sensorimotor functions., Competing Interests: TT was employed and GG-M was employed as scientists at Royal Philips, a commercial company, which is a major innovating institution in the field of health technology. As scientists, they are mandated by their employer to make meaningful scientific contributions and always strive to ensure scientific integrity in their academic writings. PD is a founder of MindAffect, a spin-off aiming to bring innovations in BCI to the medical market., (Copyright © 2023 Tsoneva, Garcia-Molina and Desain.)

Memory, one of the hallmarks of human cognition, can be modified when humans voluntarily modulate neural population activity using neurofeedback. However, it is currently unknown whether neurofeedback can influence the integration of memories, and whether memory is facilitated or impaired after such neural perturbation. In this study, participants memorized objects while we provided them with abstract neurofeedback based on their brain activity patterns in the ventral visual stream. This neurofeedback created an implicit face or house context in the brain while memorizing the objects. The results revealed that participants created associations between each memorized object and its implicit context solely due to the neurofeedback manipulation. Our findings shed light onto how memory formation can be influenced by synthetic memory tags with neurofeedback and advance our understanding of mnemonic processing., (© 2022. The Author(s).)

Objective. Code-modulated visual evoked potentials (c-VEP) have been consolidated in recent years as robust control signals capable of providing non-invasive brain-computer interfaces (BCIs) for reliable, high-speed communication. Their usefulness for communication and control purposes has been reflected in an exponential increase of related articles in the last decade. The aim of this review is to provide a comprehensive overview of the literature to gain understanding of the existing research on c-VEP-based BCIs, since its inception (1984) until today (2021), as well as to identify promising future research lines. Approach. The literature review was conducted according to the Preferred Reporting Items for Systematic reviews and Meta-Analysis guidelines. After assessing the eligibility of journal manuscripts, conferences, book chapters and non-indexed documents, a total of 70 studies were included. A comprehensive analysis of the main characteristics and design choices of c-VEP-based BCIs was discussed, including stimulation paradigms, signal processing, modeling responses, applications, etc. Main results. The literature review showed that state-of-the-art c-VEP-based BCIs are able to provide an accurate control of the system with a large number of commands, high selection speeds and even without calibration. In general, a lack of validation in real setups was observed, especially regarding the validation with disabled populations. Future work should be focused toward developing self-paced c-VEP-based portable BCIs applied in real-world environments that could exploit the unique benefits of c-VEP paradigms. Some aspects such as asynchrony, unsupervised training, or code optimization still require further research and development. Significance. Despite the growing popularity of c-VEP-based BCIs, to the best of our knowledge, this is the first literature review on the topic. In addition to providing a joint discussion of the advances in the field, some future lines of research are suggested to contribute to the development of reliable plug-and-play c-VEP-based BCIs., (© 2021 IOP Publishing Ltd.)

Objective: Brain-Computer Interface (BCI) spellers that make use of code-modulated Visual Evoked Potentials (cVEP) may provide a fast and more accurate alternative to existing visual BCI spellers for patients with Amyotrophic Lateral Sclerosis (ALS). However, so far the cVEP speller has only been tested on healthy participants., Methods: We assess the brain responses, BCI performance and user experience of the cVEP speller in 20 healthy participants and 10 ALS patients. All participants performed a cued and free spelling task, and a free selection of Yes/No answers., Results: 27 out of 30 participants could perform the cued spelling task with an average accuracy of 79% for ALS patients, 88% for healthy older participants and 94% for healthy young participants. All 30 participants could answer Yes/No questions freely, with an average accuracy of around 90%., Conclusions: With ALS patients typing on average 10 characters per minute, the cVEP speller presented in this paper outperforms other visual BCI spellers., Significance: These results support a general usability of cVEP signals for ALS patients, which may extend far beyond the tested speller to control e.g. an alarm, automatic door, or TV within a smart home., Competing Interests: Declaration of Competing Interest The authors declare the following financial interests/personal relationships which may be considered as potential competing interests: [The research was supported by the ALS Association (18-SCH-387) and the Netherlands ALS Foundation (18-SCH-391). Authors Peter Desain, Jason Farquhar, Jordy Thielen and Daniëlle Tump are or were affiliated to MindAffect: a start-up company that prototyped a variant of the cVEP BCI speller presented in this paper.], (Copyright © 2021 International Federation of Clinical Neurophysiology. Published by Elsevier B.V. All rights reserved.)

Objective. Typically, a brain-computer interface (BCI) is calibrated using user- and session-specific data because of the individual idiosyncrasies and the non-stationary signal properties of the electroencephalogram (EEG). Therefore, it is normal for BCIs to undergo a time-consuming passive training stage that prevents users from directly operating them. In this study, we systematically reduce the training data set in a stepwise fashion, to ultimately arrive at a calibration-free method for a code-modulated visually evoked potential (cVEP)-based BCI to fully eliminate the tedious training stage. Approach. In an extensive offline analysis, we compare our sophisticated encoding model with a traditional event-related potential (ERP) technique. We calibrate the encoding model in a standard way, with data limited to a single class while generalizing to all others and without any data. In addition, we investigate the feasibility of the zero-training cVEP BCI in an online setting. Main results. By adopting the encoding model, the training data can be reduced substantially, while maintaining both the classification performance as well as the explained variance of the ERP method. Moreover, with data from only one class or even no data at all, it still shows excellent performance. In addition, the zero-training cVEP BCI achieved high communication rates in an online spelling task, proving its feasibility for practical use. Significance. To date, this is the fastest zero-training cVEP BCI in the field, allowing high communication speeds without calibration while using only a few non-invasive water-based EEG electrodes. This allows us to skip the training stage altogether and spend all the valuable time on direct operation. This minimizes the session time and opens up new exciting directions for practical plug-and-play BCI. Fundamentally, these results validate that the adopted neural encoding model compresses data into event responses without the loss of explanatory power compared to using full ERPs as a template., (Creative Commons Attribution license.)

Steady-state visual evoked potentials (SSVEPs), the brain response to visual flicker stimulation, have proven beneficial in both research and clinical applications. Despite the practical advantages of stimulation at high frequencies in terms of visual comfort and safety, high frequency-SSVEPs have not received enough attention and little is known about the mechanisms behind their generation and propagation in time and space. In this study, we investigated the origin and propagation of SSVEPs in the gamma frequency band (40-60 Hz) by studying the dynamic properties of EEG in 32 subjects. Using low-resolution brain electromagnetic tomography (sLORETA) we identified the cortical sources involved in SSVEP generation in that frequency range to be in the primary visual cortex, Brodmann areas 17, 18 and 19 with minor contribution from sources in central and frontal sites. We investigated the SSVEP propagation as measured on the scalp in the framework of the existing theories regarding the neurophysiological mechanism through which the SSVEP spreads through the cortex. We found a progressive phase shift from posterior parieto-occipital sites over the cortex with a phase velocity of approx. 8-14 m/s and wavelength of about 21 and 24 cm. The SSVEP spatial properties appear sensitive to input frequency with higher stimulation frequencies showing a faster propagation speed.

In this paper, we investigate the robustness of electrophysiological responses of relatedness to multiple consecutive word stimuli (probes), in relation to an actively recollected target word. Such relatedness information could be used by a Brain Computer Interface to infer the active semantic concept on a user's mind, by integrating the knowledge of the relationship between the multiple probe words and the 'unknown' target. Such a BCI can take advantage of the N400: an event related potential that is sensitive to semantic content of a stimulus in relation to an established semantic context. However, it is unknown whether the N400 is suited for the multiple probing paradigm we propose, as other intervening words might distract from the established context (i.e., the target word). We perform an experiment in which we present up to ten words after an initial target word, and find no attenuation of the strength of the N400 in grand average ERPs and no decrease in classification accuracy for probes occurring later in the sequences. These results are groundwork for developing a BCI that infers the concept on a user's mind through repeated probing, however, low single trial decoding accuracy, and high subject variability may limit practical applicability.

Establishing non-native phoneme categories can be a notoriously difficult endeavour-in both speech perception and speech production. This study asks how these two domains interact in the course of this learning process. It investigates the effect of perceptual learning and related production practice of a challenging non-native category on the perception and/or production of that category. A four-day perceptual training protocol on the British English /æ/-/ɛ/ vowel contrast was combined with either related or unrelated production practice. After feedback on perceptual categorisation of the contrast, native Dutch participants in the related production group (N = 19) pronounced the trial's correct answer, while participants in the unrelated production group (N = 19) pronounced similar but phonologically unrelated words. Comparison of pre- and post-tests showed significant improvement over the course of training in both perception and production, but no differences between the groups were found. The lack of an effect of production practice is discussed in the light of previous, competing results and models of second-language speech perception and production. This study confirms that, even in the context of related production practice, perceptual training boosts production learning.

Healthy aging is associated with changes in many neurocognitive functions. While on the behavioral level, visual spatial attention capacities are relatively stable with increasing age, the underlying neural processes change. In this study, we investigated attention-related modulations of the stimulus-locked event-related potential (ERP) and occipital oscillations in the alpha band (8-14Hz) in young and elderly participants. Both groups performed a visual attention task equally well and the ERP showed comparable attention-related modulations in both age groups. However, in elderly subjects, oscillations in the alpha band were massively reduced both during the task and in the resting state and the typical task-related lateralized pattern of alpha activity was not observed. These differences between young and elderly participants were observed on the group level as well as on the single trial level. The results indicate that younger and older adults use different neural strategies to reach the same performance in a covert visual spatial attention task., (Copyright © 2016 Elsevier B.V. All rights reserved.)

Locomotor malfunction represents a major problem in some neurological disorders like stroke and spinal cord injury. Robot-assisted walking devices have been used during rehabilitation of patients with these ailments for regaining and improving walking ability. Previous studies showed the advantage of brain-computer interface (BCI) based robot-assisted training combined with physical therapy in the rehabilitation of the upper limb after stroke. Therefore, stroke patients with walking disorders might also benefit from using BCI robot-assisted training protocols. In order to develop such BCI, it is necessary to evaluate the feasibility to decode walking intention from cortical patterns during robot-assisted gait training. Spectral patterns in the electroencephalogram (EEG) related to robot-assisted active and passive walking were investigated in 10 healthy volunteers (mean age 32.3±10.8, six female) and in three acute stroke patients (all male, mean age 46.7±16.9, Berg Balance Scale 20±12.8). A logistic regression classifier was used to distinguish walking from baseline in these spectral EEG patterns. Mean classification accuracies of 94.0±5.4% and 93.1±7.9%, respectively, were reached when active and passive walking were compared against baseline. The classification performance between passive and active walking was 83.4±7.4%. A classification accuracy of 89.9±5.7% was achieved in the stroke patients when comparing walking and baseline. Furthermore, in the healthy volunteers modulation of low gamma activity in central midline areas was found to be associated with the gait cycle phases, but not in the stroke patients. Our results demonstrate the feasibility of BCI-based robotic-assisted training devices for gait rehabilitation.

Objective: Steady-state visual evoked potentials (SSVEPs), the brain responses to repetitive visual stimulation (RVS), are widely utilized in neuroscience. Their high signal-to-noise ratio and ability to entrain oscillatory brain activity are beneficial for their applications in brain-computer interfaces, investigation of neural processes underlying brain rhythmic activity (steady-state topography) and probing the causal role of brain rhythms in cognition and emotion. This paper aims at analyzing the space and time EEG dynamics in response to RVS at the frequency of stimulation and ongoing rhythms in the delta, theta, alpha, beta, and gamma bands., Approach: We used electroencephalography (EEG) to study the oscillatory brain dynamics during RVS at 10 frequencies in the gamma band (40-60 Hz). We collected an extensive EEG data set from 32 participants and analyzed the RVS evoked and induced responses in the time-frequency domain., Main Results: Stable SSVEP over parieto-occipital sites was observed at each of the fundamental frequencies and their harmonics and sub-harmonics. Both the strength and the spatial propagation of the SSVEP response seem sensitive to stimulus frequency. The SSVEP was more localized around the parieto-occipital sites for higher frequencies (>54 Hz) and spread to fronto-central locations for lower frequencies. We observed a strong negative correlation between stimulation frequency and relative power change at that frequency, the first harmonic and the sub-harmonic components over occipital sites. Interestingly, over parietal sites for sub-harmonics a positive correlation of relative power change and stimulation frequency was found. A number of distinct patterns in delta (1-4 Hz), theta (4-8 Hz), alpha (8-12 Hz) and beta (15-30 Hz) bands were also observed. The transient response, from 0 to about 300 ms after stimulation onset, was accompanied by increase in delta and theta power over fronto-central and occipital sites, which returned to baseline after approx. 500 ms. During the steady-state response, we observed alpha band desynchronization over occipital sites and after 500 ms also over frontal sites, while neighboring areas synchronized. The power in beta band over occipital sites increased during the stimulation period, possibly caused by increase in power at sub-harmonic frequencies of stimulation. Gamma power was also enhanced by the stimulation., Significance: These findings have direct implications on the use of RVS and SSVEPs for neural process investigation through steady-state topography, controlled entrainment of brain oscillations and BCIs. A deep understanding of SSVEP propagation in time and space and the link with ongoing brain rhythms is crucial for optimizing the typical SSVEP applications for studying, assisting, or augmenting human cognitive and sensorimotor function.

Brain-Computer Interfaces (BCIs) allow users to control devices and communicate by using brain activity only. BCIs based on broad-band visual stimulation can outperform BCIs using other stimulation paradigms. Visual stimulation with pseudo-random bit-sequences evokes specific Broad-Band Visually Evoked Potentials (BBVEPs) that can be reliably used in BCI for high-speed communication in speller applications. In this study, we report a novel paradigm for a BBVEP-based BCI that utilizes a generative framework to predict responses to broad-band stimulation sequences. In this study we designed a BBVEP-based BCI using modulated Gold codes to mark cells in a visual speller BCI. We defined a linear generative model that decomposes full responses into overlapping single-flash responses. These single-flash responses are used to predict responses to novel stimulation sequences, which in turn serve as templates for classification. The linear generative model explains on average 50% and up to 66% of the variance of responses to both seen and unseen sequences. In an online experiment, 12 participants tested a 6 × 6 matrix speller BCI. On average, an online accuracy of 86% was reached with trial lengths of 3.21 seconds. This corresponds to an Information Transfer Rate of 48 bits per minute (approximately 9 symbols per minute). This study indicates the potential to model and predict responses to broad-band stimulation. These predicted responses are proven to be well-suited as templates for a BBVEP-based BCI, thereby enabling communication and control by brain activity only.

This article investigates a possible Brain Computer Interface (BCI) based on semantic relations. The BCI determines which prime word a subject has in mind by presenting probe words using an intelligent algorithm. Subjects indicate when a presented probe word is related to the prime word by a single finger tap. The detection of the neural signal associated with this movement is used by the BCI to decode the prime word. The movement detector combined both the evoked (ERP) and induced (ERD) responses elicited with the movement. Single trial movement detection had an average accuracy of 67%. The decoding of the prime word had an average accuracy of 38% when using 100 probes and 150 possible targets, and 41% after applying a dynamic stopping criterium, reducing the average number of probes to 47. The article shows that the intelligent algorithm used to present the probe words has a significantly higher performance than a random selection of probes. Simulations demonstrate that the BCI also works with larger vocabulary sizes, and the performance scales logarithmically with vocabulary size.