Brain-computer interface based on generation of visual images

Evaluating the Feasibility of Visual Imagery for an EEG-Based Brain-Computer Interface

Visual imagery, or the mental simulation of visual information from memory, could serve as an effective control paradigm for a brain-computer interface (BCI) due to its ability to directly convey the user's intention with many natural ways of envisioning an intended action. However, multiple initial investigations into using visual imagery as a BCI control strategies have been unable to fully evaluate the capabilities of true spontaneous visual mental imagery. One major limitation in these prior works is that the target image is typically displayed immediately preceding the imagery period. This paradigm does not capture spontaneous mental imagery as would be necessary in an actual BCI application but something more akin to short-term retention in visual working memory. Results from the present study show that short-term visual imagery following the presentation of a specific target image provides a stronger, more easily classifiable neural signature in EEG than spontaneous visual imagery from long-term memory following an auditory cue for the image. We also show that short-term visual imagery and visual perception share commonalities in the most predictive electrodes and spectral features. However, visual imagery received greater influence from frontal electrodes whereas perception was mostly confined to occipital electrodes. This suggests that visual perception is primarily driven by sensory information whereas visual imagery has greater contributions from areas associated with memory and attention. This work provides the first direct comparison of short-term and long-term visual imagery tasks and provides greater insight into the feasibility of using visual imagery as a BCI control strategy.

Novel stability approach using Routh-Hurwitz criterion for brain computer interface applications

BACKGROUND

The stability criterion approach is very important for estimating precise behavior before or after fabricating brain computer interface system applications.

OBJECTIVE

A novel approach using the Routh-Hurwitz standard criterion method is proposed to easily determine and analyze the stability of brain computer interface system applications. Using this developed approach, we were able to easily test the stability of technical issue using simple programmed codes before or after brain computer interfaces fabrication applications.

METHODS

Using a MATLAB simulation program package, we are able to provide two different special case examples such as a first zero element and a row of zeros to verify the capability of our proposed Routh-Hurwitz method.

RESULTS

The MATLAB simulation program provided efficient Routh-Hurwitz standard criterion results by differentiating the highest coefficients of the s and a.

CONCLUSION

This technical paper explains how to use our proposed new Routh-Hurwitz standard condition to simply ascertain and determine the brain computer interface system stability without customized commercial simulation tools.

Measuring brain potentials of imagination linked to physiological needs and motivational states

Introduction

While EEG signals reflecting motor and perceptual imagery are effectively used in brain computer interface (BCI) contexts, little is known about possible indices of motivational states. In the present study, electrophysiological markers of imagined motivational states, such as craves and desires were investigated.

Methods

Event-related potentials (ERPs) were recorded in 31 participants during perception and imagery elicited by the presentation of 360 pictograms. Twelve micro-categories of needs, subdivided into four macro-categories, were considered as most relevant for a possible BCI usage, namely: primary visceral needs (e.g., hunger, linked to desire of food); somatosensory thermal and pain sensations (e.g., cold, linked to desire of warm), affective states (e.g., fear: linked to desire of reassurance) and secondary needs (e.g., desire to exercise or listen to music). Anterior N400 and centroparietal late positive potential (LPP) were measured and statistically analyzed.

Results

N400 and LPP were differentially sensitive to the various volition stats, depending on their sensory, emotional and motivational poignancy. N400 was larger to imagined positive appetitive states (e.g., play, cheerfulness) than negative ones (sadness or fear). In addition, N400 was of greater amplitude during imagery of thermal and nociceptive sensations than other motivational or visceral states. Source reconstruction of electromagnetic dipoles showed the activation of sensorimotor areas and cerebellum for movement imagery, and of auditory and superior frontal areas for music imagery.

Discussion

Overall, ERPs were smaller and more anteriorly distributed during imagery than perception, but showed some similarity in terms of lateralization, distribution, and category response, thus indicating some overlap in neural processing, as also demonstrated by correlation analyses. In general, anterior frontal N400 provided clear markers of subjects’ physiological needs and motivational states, especially cold, pain, and fear (but also sadness, the urgency to move, etc.), than can signal life-threatening conditions. It is concluded that ERP markers might potentially allow the reconstruction of mental representations related to various motivational states through BCI systems.

Improving classification and reconstruction of imagined images from EEG signals

Decoding brain activity related to specific tasks, such as imagining something, is important for brain computer interface (BCI) control. While decoding of brain signals, such as functional magnetic resonance imaging (fMRI) signals and electroencephalography (EEG) signals, during observing visual images and while imagining images has been previously reported, further development of methods for improving training, performance, and interpretation of brain data was the goal of this study. We applied a Sinc-EEGNet to decode brain activity during perception and imagination of visual stimuli, and added an attention module to extract the importance of each electrode or frequency band. We also reconstructed images from brain activity by using a generative adversarial network (GAN). By combining the EEG recorded during a visual task (perception) and an imagination task, we have successfully boosted the accuracy of classifying EEG data in the imagination task and improved the quality of reconstruction by GAN. Our result indicates that the brain activity evoked during the visual task is present in the imagination task and can be used for better classification of the imagined image. By using the attention module, we can derive the spatial weights in each frequency band and contrast spatial or frequency importance between tasks from our model. Imagination tasks are classified by low frequency EEG signals over temporal cortex, while perception tasks are classified by high frequency EEG signals over occipital and frontal cortex. Combining data sets in training results in a balanced model improving classification of the imagination task without significantly changing performance in the visual task. Our approach not only improves performance and interpretability but also potentially reduces the burden on training since we can improve the accuracy of classifying a relatively hard task with high variability (imagination) by combining with the data of the relatively easy task, observing visual images.

Black hole algorithm with convolutional neural networks for the creation of brain-computer interface based in visual perception and visual imagery

Non-invasive brain-computer interfaces can be implemented through different paradigms, the most used one being motor imagery and evoked potentials, although recently there has been an interest in paradigms based on perception and visual imagery. Following this approach, this work demonstrates the classification of visual imagery, visual perception and also the possibility of knowledge transfer between these two domains from EEG signals using convolutional neural networks. Also, we propose an adequate framework for such classification, which uses convolutional neural networks and the black hole heuristic algorithm for the search for optimal neural network structures.

Power Spectrum and Connectivity Analysis in EEG Recording during Attention and Creativity Performance in Children

The present research aims at examining the power spectrum and exploring functional brain connectivity/disconnectivity during concentration performance, as measured by the d2 test of attention and creativity as measured by the CREA test in typically developing children. To this end, we examined brain connectivity by using phase synchrony (i.e., phase locking index (PLI) over the EEG signals acquired by the Emotiv EPOC neuroheadset in 15 children aged 9- to 12-years. Besides, as a complement, a power spectrum analysis of the acquired signals was performed. Our results indicated that, during d2 Test performance there was an increase in global gamma phase synchronization and there was a global alpha and theta band desynchronization. Conversely, during CREA task, power spectrum analysis showed a significant increase in the delta, beta, theta, and gamma bands. Connectivity analysis revealed marked synchronization in theta, alpha, and gamma. These findings are consistent with other neuroscience research indicating that multiple brain mechanisms are indeed involved in creativity. In addition, these results have important implications for the assessment of attention functions and creativity in clinical and research settings, as well as for neurofeedback interventions in children with typical and atypical development.

Deep-learning online EEG decoding brain-computer interface using error-related potentials recorded with a consumer-grade headset

Objective:Brain-computer interfaces (BCIs) allow subjects with sensorimotor disability to interact with the environment. Non-invasive BCIs relying on EEG signals such as event-related potentials (ERPs) have been established as a reliable compromise between spatio-temporal resolution and patient impact, but limitations due to portability and versatility preclude their broad application. Here we describe a deep-learning augmented error-related potential (ErrP) discriminating BCI using a consumer-grade portable headset EEG, the Emotiv EPOC+.Approach:We recorded and discriminated ErrPs offline and online from 14 subjects during a visual feedback task.Main results:We achieved online discrimination accuracies of up to 81%, comparable to those obtained with professional 32/64-channel EEG devices via deep-learning using either a generative-adversarial network or an intrinsic-mode function augmentation of the training data and minimalistic computing resources.Significance:Our BCI model has the potential of expanding the spectrum of BCIs to more portable, artificial intelligence-enhanced, efficient interfaces accelerating the routine deployment of these devices outside the controlled environment of a scientific laboratory.

Political Orientation as Psychological Defense or Basic Disposition? A Social Neuroscience Examination

Psychological views on political orientation generally agree that conservatism is associated with negativity bias but disagree on the form of that association. Some view conservatism as a psychological defense that insulates from negative stimuli and events. Others view conservatism as a consequence of increased dispositional sensitivity to negative stimuli and events. Further complicating matters, research shows that conservatives are sometimes more and sometimes less sensitive to negative stimuli and events. The current research integrates these opposing views and results. We reasoned that conservatives should typically be less sensitive to negative stimuli if conservative beliefs act as a psychological defense. However, when core components of conservative beliefs are threatened, the psychological defense may fall, and conservatives may show heightened sensitivity to negative stimuli. In two ERP studies, participants were randomly assigned to either an ostensibly real economic threat or a nonthreatening control condition. To measure reactivity to negative stimuli, we indexed the P3 component to aversive white noise bursts in an auditory oddball paradigm. In both studies, the relationship between increased conservatism and P3 mean amplitude was negative in the control condition but positive in threat condition (this relationship was stronger in Study 2). In Study 2, source localization of the P3 component revealed that, after threat, conservatism was associated with increased activity in the anterior cingulate cortex and dorsomedial prefrontal cortex, regions associated with conflict-related processes. These results demonstrate that the link between conservatism and negativity bias is context-dependent, i.e., dependent on threat experiences.

Brain-Computer Interface: Advancement and Challenges

Brain-Computer Interface (BCI) is an advanced and multidisciplinary active research domain based on neuroscience, signal processing, biomedical sensors, hardware, etc. Since the last decades, several groundbreaking research has been conducted in this domain. Still, no comprehensive review that covers the BCI domain completely has been conducted yet. Hence, a comprehensive overview of the BCI domain is presented in this study. This study covers several applications of BCI and upholds the significance of this domain. Then, each element of BCI systems, including techniques, datasets, feature extraction methods, evaluation measurement matrices, existing BCI algorithms, and classifiers, are explained concisely. In addition, a brief overview of the technologies or hardware, mostly sensors used in BCI, is appended. Finally, the paper investigates several unsolved challenges of the BCI and explains them with possible solutions.

Nonspecific Visuospatial Imagery as a Novel Mental Task for Online EEG-Based BCI Control

Brain-computer interfaces (BCIs) can provide a means of communication to individuals with severe motor disorders, such as those presenting as locked-in. Many BCI paradigms rely on motor neural pathways, which are often impaired in these individuals. However, recent findings suggest that visuospatial function may remain intact. This study aimed to determine whether visuospatial imagery, a previously unexplored task, could be used to signify intent in an online electroencephalography (EEG)-based BCI. Eighteen typically developed participants imagined checkerboard arrow stimuli in four quadrants of the visual field in 5-s trials, while signals were collected using 16 dry electrodes over the visual cortex. In online blocks, participants received graded visual feedback based on their performance. An initial BCI pipeline (visuospatial imagery classifier I) attained a mean accuracy of [Formula: see text]% classifying rest against visuospatial imagery in online trials. This BCI pipeline was further improved using restriction to alpha band features (visuospatial imagery classifier II), resulting in a mean pseudo-online accuracy of [Formula: see text]%. Accuracies exceeded the threshold for practical BCIs in 12 participants. This study supports the use of visuospatial imagery as a real-time, binary EEG-BCI control paradigm.

Frontal alpha asymmetry in schizotypy: electrophysiological evidence for motivational dysfunction

Introduction: Schizotypy is defined as personality traits reflecting an underlying risk for schizophrenia-spectrum disorders. As yet, there is a dearth of suitable objective markers for measuring schizotypy. Frontal alpha asymmetry, characterised by reduced left versus right frontal region activity, reflects trait-like diminished approach-related systems and has been found in schizophrenia. Methods: The present study used electroencephalography (EEG) recorded on a consumer-grade mobile headset to examine asymmetric resting-state frontal alpha, beta, and gamma power within the multidimensional schizotypy (e.g. positive, negative, disorganised) during a three-minute "eyes closed" resting period in college undergraduates (n=49). Results: Findings suggest that schizotypy was exclusively related to reduced left versus right-lateralised power in the alpha frequency (8.1-12.9 Hz., R²= .16). Follow-up analysis suggested that positive schizotypy was uniquely associated with increased right alpha activity, indicating increased withdrawal motivation. Conclusions: Frontal asymmetry is a possible ecologically valid objective marker for schizotypy that may be detectable using easily accessible, consumer-grade technology.

Convolutional neural networks and genetic algorithm for visual imagery classification

Brain-Computer Interface (BCI) systems establish a channel for direct communication between the brain and the outside world without having to use the peripheral nervous system. While most BCI systems use evoked potentials and motor imagery, in the present work we present a technique that employs visual imagery. Our technique uses neural networks to classify the signals produced in visual imagery. To this end, we have used densely connected neural and convolutional networks, together with a genetic algorithm to find the best parameters for these networks. The results we obtained are a 60% success rate in the classification of four imagined objects (a tree, a dog, an airplane and a house) plus a state of relaxation, thus outperforming the state of the art in visual imagery classification.

Validation of a low-cost EEG device in detecting neural correlates of social conformity

The study of conformity from a neurobiological point of view has interested many authors: among them, Shestakova and colleagues (2013) have showed how conformity can be assessed through the analysis of event related potentials (ERPs). More specifically, the P300 component of the ERP was shown to be sensitive to the behavioral adjustment that an individual makes when not agreeing with the majority of a group. Starting from these contributions, in the present study, the famous experiment of Solomon Asch [1] was replicated online. The experiment was conducted on a sample of university students, using an innovative and low-cost tool capable of recording the brain signal (a wireless headset equipped with fourteen electrodes, called Emotiv EPOC). The present research aims to demonstrate how cheap and little sensitive tools enable the detection of ERP components that characterize social conformity in an ecological context.

Social Media Approval Reduces Emotional Arousal for People High in Narcissism: Electrophysiological Evidence

We used event-related potentials (ERPs) to examine if posting a "selfie" and receiving validation from others in the form of "likes" on social media can help narcissists reduce psychological distress. After all participants completed the narcissistic personality inventory (NPI) and experienced social exclusion, participants completed an auditory startle task that elicits the P3 to white noise-an ERP component that reflects emotional arousal and is sensitive to psychological distress. Participants were then randomly assigned to either view a personal "selfie" that quickly received a significant number of ostensibly real "likes" (selfie with likes condition), view a "selfie" with no feedback (selfie only condition), or view a neutral picture before (neutral picture condition) completing the auditory startle task again. Results revealed that participants high on the Leadership/Authority subscale of the NPI in the "selfie" with "likes" condition demonstrated a pre-post manipulation decrease in P3 mean amplitude, relative to participants in the other two conditions. These results suggest that approval via social media can help certain kinds of narcissists alleviate distress from social exclusion.

Recommendations for Developing an EEG Laboratory at a Primarily Undergraduate Institution

Given its relatively low cost and minimal required space, an EEG laboratory provides the most feasible human cognitive neuroscience technique to implement at primarily undergraduate institutions (PUI). However, neuroscience programs at PUIs may be deterred from incorporating EEG methods into their research programs and/or classrooms due to limited funds and resources. This article provides recommended guidelines for faculty researchers looking to set up an EEG lab at their host PUIs with an emphasis on feasibility. We offer considerations regarding infrastructure, equipment, personnel, and potential sources of funding. A case study is also provided, describing the successful implementation and development of an EEG/ERP lab at a Midwest PUI, Baldwin Wallace University. Our goal is to offer diverse options for starting a new, or revitalizing an existing, EEG lab. We contend that such a laboratory at a PUI will advance undergraduate students' access to interdisciplinary neuroscience research and curricular opportunities.

BIARAM: A process for analyzing correlated brain regions using association rule mining

BACKGROUND AND OBJECTIVE

Because examining correlated (vs. individual) brain activity is a superior method for locating neural correlates of a stimulus, using a network approach for analyzing brain activity is gaining interest. In this study, we propose and illustrate the use of association rule mining (ARM) to analyze brain regions that are activated simultaneously. ARM is commonly used in marketing and other disciplines to help determine items that might be purchased together. We apply this technique toward identifying correlated brain regions that may respond simultaneously to specific stimuli. Our objective is to introduce ARM, describe a process for converting neural images into viable datasets (for analyses), and suggest how to apply this process for generating insights about the brain's responses to specific stimuli (e.g. technology-associated interruptions).

METHODS

We analyze electroencephalogram (EEG) data collected from 46 participants; convert brain waves into images via a source localization algorithm known as sLORETA (i.e., standardized low-resolution brain electromagnetic tomography); reorganize these into a "transactional" dataset; and generate association rules through ARM.

RESULTS

We compare the results with more conventional methods for analyzing neuroimaging data. We show that there is a stronger correlation between frontal lobe and sublobar/insula regions after interruptions. This result would not be obvious from independent analysis of each region.

CONCLUSIONS

The main contribution of this paper is introducing ARM as a method for analyzing multiple images. We suggest that the biomedical community may apply this commonly available data mining technique to develop further insights about correlated regions affected by specific stimuli.

Improving the quality of a collective signal in a consumer EEG headset

This work focuses on the experimental data analysis of electroencephalography (EEG) data, in which multiple sensors are recording oscillatory voltage time series. The EEG data analyzed in this manuscript has been acquired using a low-cost commercial headset, the Emotiv EPOC+. Our goal is to compare different techniques for the optimal estimation of collective rhythms from EEG data. To this end, a traditional method such as the principal component analysis (PCA) is compared to more recent approaches to extract a collective rhythm from phase-synchronized data. Here, we extend the work by Schwabedal and Kantz (PRL 116, 104101 (2016)) evaluating the performance of the Kosambi-Hilbert torsion (KHT) method to extract a collective rhythm from multivariate oscillatory time series and compare it to results obtained from PCA. The KHT method takes advantage of the singular value decomposition algorithm and accounts for possible phase lags among different time series and allows to focus the analysis on a specific spectral band, optimally amplifying the signal-to-noise ratio of a common rhythm. We evaluate the performance of these methods for two particular sets of data: EEG data recorded with closed eyes and EEG data recorded while observing a screen flickering at 15 Hz. We found an improvement in the signal-to-noise ratio of the collective signal for the KHT over the PCA, particularly when random temporal shifts are added to the channels.

Toward an Open-Ended BCI: A User-Centered Coadaptive Design

Brain-computer interfaces (BCIs) allow users to control a device by interpreting their brain activity. For simplicity, these devices are designed to be operated by purposefully modulating specific predetermined neurophysiological signals, such as the sensorimotor rhythm. However, the ability to modulate a given neurophysiological signal is highly variable across individuals, contributing to the inconsistent performance of BCIs for different users. These differences suggest that individuals who experience poor BCI performance with one class of brain signals might have good results with another. In order to take advantage of individual abilities as they relate to BCI control, we need to move beyond the current approaches. In this letter, we explore a new BCI design aimed at a more individualized and user-focused experience, which we call open-ended BCI. Individual users were given the freedom to discover their own mental strategies as opposed to being trained to modulate a given brain signal. They then underwent multiple coadaptive training sessions with the BCI. Our first open-ended BCI performed similarly to comparable BCIs while accommodating a wider variety of mental strategies without a priori knowledge of the specific brain signals any individual might use. Post hoc analysis revealed individual differences in terms of which sensory modality yielded optimal performance. We found a large and significant effect of individual differences in background training and expertise, such as in musical training, on BCI performance. Future research should be focused on finding more generalized solutions to user training and brain state decoding methods to fully utilize the abilities of different individuals in an open-ended BCI. Accounting for each individual's areas of expertise could have important implications on BCI training and BCI application design.

Analysis of functional brain connections for positive-negative emotions using phase locking value

In this study, we investigate the brain networks during positive and negative emotions for different types of stimulus (audio only, video only and audio + video) in [Formula: see text], and [Formula: see text] bands in terms of phase locking value, a nonlinear method to study functional connectivity. Results show notable hemispheric lateralization as phase synchronization values between channels are significant and high in right hemisphere for all emotions. Left frontal electrodes are also found to have control over emotion in terms of functional connectivity. Besides significant inter-hemisphere phase locking values are observed between left and right frontal regions, specifically between left anterior frontal and right mid-frontal, inferior-frontal and anterior frontal regions; and also between left and right mid frontal regions. ANOVA analysis for stimulus types show that stimulus types are not separable for emotions having high valence. PLV values are significantly different only for negative emotions or neutral emotions between audio only/video only and audio only/audio + video stimuli. Finding no significant difference between video only and audio + video stimuli is interesting and might be interpreted as that video content is the most effective part of a stimulus.

EEG to Primary Rewards: Predictive Utility and Malleability by Brain Stimulation

Theta burst stimulation (TBS) is thought to affect reward processing mechanisms, which may increase and decrease reward sensitivity. To test the ability of TBS to modulate response to strong primary rewards, participants hypersensitive to primary rewards were recruited. Twenty men and women with at least two opposite-sex, sexual partners in the last year received two forms of TBS. Stimulations were randomized to avoid order effects and separated by 2 hours to reduce carryover. The two TBS forms have been demonstrated to inhibit (continuous) or excite (intermittent) the left dorsolateral prefrontal cortex using different pulse patterns, which links to brain areas associated with reward conditioning. After each TBS, participants completed tasks assessing their reward responsiveness to monetary and sexual rewards. Electroencephalography (EEG) was recorded. They also reported their number of orgasms in the weekend following stimulation. This signal was malleable by TBS, where excitatory TBS resulted in lower EEG alpha relative to inhibitory TBS to primary rewards. EEG responses to sexual rewards in the lab (following both forms of TBS) predicted the number of orgasms experienced over the forthcoming weekend. TBS may be useful in modifying hypersensitivity or hyposensitivity to primary rewards that predict sexual behaviors. Since TBS altered the anticipation of a sexual reward, TBS may offer a novel treatment for sexual desire problems.

Inhibition Underlies the Effect of High Need for Closure on Cultural Closed-Mindedness under Mortality Salience

The hypothesis that people respond to reminders of mortality with closed-minded, ethnocentric attitudes has received extensive empirical support, largely from research in the Terror Management Theory (TMT) tradition. However, the basic motivational and neural processes that underlie this effect remain largely hypothetical. According to recent neuropsychological theorizing, mortality salience (MS) effects on cultural closed-mindedness may be mediated by activity in the behavioral inhibition system (BIS), which leads to passive avoidance and decreased approach motivation. This should be especially true for people motivated to avoid unfamiliar and potentially threatening stimuli as reflected in a high need for closure (NFC). In two studies involving moderated mediation analyses, people high on trait NFC responded to MS with increased BIS activity (as indicated by EEG and the line bisection task), which is characteristic of inhibited approach motivation. BIS activity, in turn, predicted a reluctance to explore foreign cultures (Study 1) and generalized ethnocentric attitudes (Study 2). In a third study, inhibition was induced directly and caused an increase in ethnocentrism for people high on NFC. Moreover, the effect of the inhibition manipulation × NFC interaction on ethnocentrism was explained by increases in BIS-related affect (i.e., anxious inhibition) at high NFC. To our knowledge, this research is the first to establish an empirical link between very basic, neurally-instantiated inhibitory processes and rather complex, higher-order manifestations of intergroup negativity in response to MS. Our findings contribute to a fuller understanding of the cultural worldview defense phenomenon by illuminating the motivational underpinnings of cultural closed-mindedness in the wake of existential threat.

Brain-Computer Interface Controlled Cyborg: Establishing a Functional Information Transfer Pathway from Human Brain to Cockroach Brain

An all-chain-wireless brain-to-brain system (BTBS), which enabled motion control of a cyborg cockroach via human brain, was developed in this work. Steady-state visual evoked potential (SSVEP) based brain-computer interface (BCI) was used in this system for recognizing human motion intention and an optimization algorithm was proposed in SSVEP to improve online performance of the BCI. The cyborg cockroach was developed by surgically integrating a portable microstimulator that could generate invasive electrical nerve stimulation. Through Bluetooth communication, specific electrical pulse trains could be triggered from the microstimulator by BCI commands and were sent through the antenna nerve to stimulate the brain of cockroach. Serial experiments were designed and conducted to test overall performance of the BTBS with six human subjects and three cockroaches. The experimental results showed that the online classification accuracy of three-mode BCI increased from 72.86% to 78.56% by 5.70% using the optimization algorithm and the mean response accuracy of the cyborgs using this system reached 89.5%. Moreover, the results also showed that the cyborg could be navigated by the human brain to complete walking along an S-shape track with the success rate of about 20%, suggesting the proposed BTBS established a feasible functional information transfer pathway from the human brain to the cockroach brain.

Assessing the feasibility of online SSVEP decoding in human walking using a consumer EEG headset

BACKGROUND

Bridging the gap between laboratory brain-computer interface (BCI) demonstrations and real-life applications has gained increasing attention nowadays in translational neuroscience. An urgent need is to explore the feasibility of using a low-cost, ease-of-use electroencephalogram (EEG) headset for monitoring individuals' EEG signals in their natural head/body positions and movements. This study aimed to assess the feasibility of using a consumer-level EEG headset to realize an online steady-state visual-evoked potential (SSVEP)-based BCI during human walking.

METHODS

This study adopted a 14-channel Emotiv EEG headset to implement a four-target online SSVEP decoding system, and included treadmill walking at the speeds of 0.45, 0.89, and 1.34 meters per second (m/s) to initiate the walking locomotion. Seventeen participants were instructed to perform the online BCI tasks while standing or walking on the treadmill. To maintain a constant viewing distance to the visual targets, participants held the hand-grip of the treadmill during the experiment. Along with online BCI performance, the concurrent SSVEP signals were recorded for offline assessment.

RESULTS

Despite walking-related attenuation of SSVEPs, the online BCI obtained an information transfer rate (ITR) over 12 bits/min during slow walking (below 0.89 m/s).

CONCLUSIONS

SSVEP-based BCI systems are deployable to users in treadmill walking that mimics natural walking rather than in highly-controlled laboratory settings. This study considerably promotes the use of a consumer-level EEG headset towards the real-life BCI applications.

Assessing the quality of steady-state visual-evoked potentials for moving humans using a mobile electroencephalogram headset

Recent advances in mobile electroencephalogram (EEG) systems, featuring non-prep dry electrodes and wireless telemetry, have enabled and promoted the applications of mobile brain-computer interfaces (BCIs) in our daily life. Since the brain may behave differently while people are actively situated in ecologically-valid environments versus highly-controlled laboratory environments, it remains unclear how well the current laboratory-oriented BCI demonstrations can be translated into operational BCIs for users with naturalistic movements. Understanding inherent links between natural human behaviors and brain activities is the key to ensuring the applicability and stability of mobile BCIs. This study aims to assess the quality of steady-state visual-evoked potentials (SSVEPs), which is one of promising channels for functioning BCI systems, recorded using a mobile EEG system under challenging recording conditions, e.g., walking. To systematically explore the effects of walking locomotion on the SSVEPs, this study instructed subjects to stand or walk on a treadmill running at speeds of 1, 2, and 3 mile (s) per hour (MPH) while concurrently perceiving visual flickers (11 and 12 Hz). Empirical results of this study showed that the SSVEP amplitude tended to deteriorate when subjects switched from standing to walking. Such SSVEP suppression could be attributed to the walking locomotion, leading to distinctly deteriorated SSVEP detectability from standing (84.87 ± 13.55%) to walking (1 MPH: 83.03 ± 13.24%, 2 MPH: 79.47 ± 13.53%, and 3 MPH: 75.26 ± 17.89%). These findings not only demonstrated the applicability and limitations of SSVEPs recorded from freely behaving humans in realistic environments, but also provide useful methods and techniques for boosting the translation of the BCI technology from laboratory demonstrations to practical applications.

Creating the feedback loop: closed-loop neurostimulation

Current DBS therapy delivers a train of electrical pulses at set stimulation parameters. This open-loop design is effective for movement disorders, but therapy may be further optimized by a closed loop design. The technology to record biosignals has outpaced our understanding of their relationship to the clinical state of the whole person. Neuronal oscillations may represent or facilitate the cooperative functioning of brain ensembles, and may provide critical information to customize neuromodulation therapy. This review addresses advances to date, not of the technology per se, but of the strategies to apply neuronal signals to trigger or modulate stimulation systems.

A low-cost EEG system-based hybrid brain-computer interface for humanoid robot navigation and recognition

This paper describes a hybrid brain-computer interface (BCI) technique that combines the P300 potential, the steady state visually evoked potential (SSVEP), and event related de-synchronization (ERD) to solve a complicated multi-task problem consisting of humanoid robot navigation and control along with object recognition using a low-cost BCI system. Our approach enables subjects to control the navigation and exploration of a humanoid robot and recognize a desired object among candidates. This study aims to demonstrate the possibility of a hybrid BCI based on a low-cost system for a realistic and complex task. It also shows that the use of a simple image processing technique, combined with BCI, can further aid in making these complex tasks simpler. An experimental scenario is proposed in which a subject remotely controls a humanoid robot in a properly sized maze. The subject sees what the surrogate robot sees through visual feedback and can navigate the surrogate robot. While navigating, the robot encounters objects located in the maze. It then recognizes if the encountered object is of interest to the subject. The subject communicates with the robot through SSVEP and ERD-based BCIs to navigate and explore with the robot, and P300-based BCI to allow the surrogate robot recognize their favorites. Using several evaluation metrics, the performances of five subjects navigating the robot were quite comparable to manual keyboard control. During object recognition mode, favorite objects were successfully selected from two to four choices. Subjects conducted humanoid navigation and recognition tasks as if they embodied the robot. Analysis of the data supports the potential usefulness of the proposed hybrid BCI system for extended applications. This work presents an important implication for the future work that a hybridization of simple BCI protocols provide extended controllability to carry out complicated tasks even with a low-cost system.

Performance of the Emotiv Epoc headset for P300-based applications

Background

For two decades, EEG-based Brain-Computer Interface (BCI) systems have been widely studied in research labs. Now, researchers want to consider out-of-the-lab applications and make this technology available to everybody. However, medical-grade EEG recording devices are still much too expensive for end-users, especially disabled people. Therefore, several low-cost alternatives have appeared on the market. The Emotiv Epoc headset is one of them. Although some previous work showed this device could suit the customer’s needs in terms of performance, no quantitative classification-based assessments compared to a medical system are available.

Methods

This paper aims at statistically comparing a medical-grade system, the ANT device, and the Emotiv Epoc headset by determining their respective performances in a P300 BCI using the same electrodes. On top of that, a review of previous Emotiv studies and a discussion on practical considerations regarding both systems are proposed. Nine healthy subjects participated in this experiment during which the ANT and the Emotiv systems are used in two different conditions: sitting on a chair and walking on a treadmill at constant speed.

Results

The Emotiv headset performs significantly worse than the medical device; observed effect sizes vary from medium to large. The Emotiv headset has higher relative operational and maintenance costs than its medical-grade competitor.

Conclusions

Although this low-cost headset is able to record EEG data in a satisfying manner, it should only be chosen for non critical applications such as games, communication systems, etc. For rehabilitation or prosthesis control, this lack of reliability may lead to serious consequences. For research purposes, the medical system should be chosen except if a lot of trials are available or when the Signal-to-Noise Ratio is high. This also suggests that the design of a specific low-cost EEG recording system for critical applications and research is still required.

Multimodal detection of head-movement artefacts in EEG

Artefacts arising from head movements have been a considerable obstacle in the deployment of automatic event detection systems in ambulatory EEG. Recently, gyroscopes have been identified as a useful modality for providing complementary information to the head movement artefact detection task. In this work, a comprehensive data fusion analysis is conducted to investigate how EEG and gyroscope signals can be most effectively combined to provide a more accurate detection of head-movement artefacts in the EEG. To this end, several methods of combining these physiological and physical signals at the feature, decision and score fusion levels are examined. Results show that combination at the feature, score and decision levels is successful in improving classifier performance when compared to individual EEG or gyroscope classifiers, thus confirming that EEG and gyroscope signals carry complementary information regarding the detection of head-movement artefacts in the EEG. Feature fusion and the score fusion using the sum-rule provided the greatest improvement in artefact detection. By extending multimodal head-movement artefact detection to the score and decision fusion domains, it is possible to implement multimodal artefact detection in environments where gyroscope signals are intermittently available.

Performance assessment in brain-computer interface-based augmentative and alternative communication

A large number of incommensurable metrics are currently used to report the performance of brain-computer interfaces (BCI) used for augmentative and alterative communication (AAC). The lack of standard metrics precludes the comparison of different BCI-based AAC systems, hindering rapid growth and development of this technology. This paper presents a review of the metrics that have been used to report performance of BCIs used for AAC from January 2005 to January 2012. We distinguish between Level 1 metrics used to report performance at the output of the BCI Control Module, which translates brain signals into logical control output, and Level 2 metrics at the Selection Enhancement Module, which translates logical control to semantic control. We recommend that: (1) the commensurate metrics Mutual Information or Information Transfer Rate (ITR) be used to report Level 1 BCI performance, as these metrics represent information throughput, which is of interest in BCIs for AAC; 2) the BCI-Utility metric be used to report Level 2 BCI performance, as it is capable of handling all current methods of improving BCI performance; (3) these metrics should be supplemented by information specific to each unique BCI configuration; and (4) studies involving Selection Enhancement Modules should report performance at both Level 1 and Level 2 in the BCI system. Following these recommendations will enable efficient comparison between both BCI Control and Selection Enhancement Modules, accelerating research and development of BCI-based AAC systems.

Real-time mental arithmetic task recognition from EEG signals

Electroencephalography (EEG)-based monitoring the state of the user's brain functioning and giving her/him the visual/audio/tactile feedback is called neurofeedback technique, and it could allow the user to train the corresponding brain functions. It could provide an alternative way of treatment for some psychological disorders such as attention deficit hyperactivity disorder (ADHD), where concentration function deficit exists, autism spectrum disorder (ASD), or dyscalculia where the difficulty in learning and comprehending the arithmetic exists. In this paper, a novel method for multifractal analysis of EEG signals named generalized Higuchi fractal dimension spectrum (GHFDS) was proposed and applied in mental arithmetic task recognition from EEG signals. Other features such as power spectrum density (PSD), autoregressive model (AR), and statistical features were analyzed as well. The usage of the proposed fractal dimension spectrum of EEG signal in combination with other features improved the mental arithmetic task recognition accuracy in both multi-channel and one-channel subject-dependent algorithms up to 97.87% and 84.15% correspondingly. Based on the channel ranking, four channels were chosen which gave the accuracy up to 97.11%. Reliable real-time neurofeedback system could be implemented based on the algorithms proposed in this paper.

Hybrid EEG and eye movement interface to multi-directional target selection

This work addresses the development of a low-cost hybrid interface with eye tracking and brain signals. Eye movement detection is used for search task and EEG-based brain computer interface (BCI) for selection task. Multi-directional target selection experiments with the hybrid interface device were conducted with five subjects to evaluate the proposed hybrid interface scheme. The task asked each user to move a cursor onto a circular target among twelve possible positions and select it. Using the Fitts' law, the interface performance was compared with the computer mouse. With two BCI selection confirmation schemes, the hybrid interface attained 2-2.7 bit/s overall. Based on the results, the potential of the proposed hybrid interface was discussed.

Neurological evidence linguistic processes precede perceptual simulation in conceptual processing

There is increasing evidence from response time experiments that language statistics and perceptual simulations both play a role in conceptual processing. In an EEG experiment we compared neural activity in cortical regions commonly associated with linguistic processing and visual perceptual processing to determine to what extent symbolic and embodied accounts of cognition applied. Participants were asked to determine the semantic relationship of word pairs (e.g., sky – ground) or to determine their iconic relationship (i.e., if the presentation of the pair matched their expected physical relationship). A linguistic bias was found toward the semantic judgment task and a perceptual bias was found toward the iconicity judgment task. More importantly, conceptual processing involved activation in brain regions associated with both linguistic and perceptual processes. When comparing the relative activation of linguistic cortical regions with perceptual cortical regions, the effect sizes for linguistic cortical regions were larger than those for the perceptual cortical regions early in a trial with the reverse being true later in a trial. These results map upon findings from other experimental literature and provide further evidence that processing of concept words relies both on language statistics and on perceptual simulations, whereby linguistic processes precede perceptual simulation processes.