Brain-computer Interface Illiteracy Research Articles

Comparison of subject-independent and subject-specific EEG-based BCI using LDA and SVM classifiers

Motor imagery brain-computer interface (MI-BCI) is one of the most used paradigms in EEG-based brain-computer interface (BCI). The current state-of-the-art in BCI involves tuning classifiers to subject-specific training data, acquired over several sessions, in order to perform calibration prior to actual use of the so-called subject-specific BCI system (SS-BCI). Herein, the goal is to provide a ready-to-use system requiring minimal effort for setup. Thus, our challenge was to design a subject-independent BCI (SI-BCI) to be used by any new user without the constraint of individual calibration. Outcomes from other studies with the same purpose were used to undertake comparisons and validate our findings. For the EEG signal processing, we used a combination of the delta (0.5-4Hz), alpha (8-13Hz), and beta+gamma (13-40Hz) bands at a stage prior to feature extraction. Next, we extracted features from the 27-channel EEG using common spatial pattern (CSP) and performed binary classification (MI of right- and left-hand) with linear discriminant analysis (LDA) and support vector machine (SVM) classifiers. These analyses were done for both the SS-BCI and SI-BCI models. We employed "leave-one-subject-out" (LOSO) arrangement and 10-fold cross-validation to evaluate our SI-BCI and SS-BCI systems, respectively. Compared with other two studies, our work was the only one that showed higher accuracy for the LDA classifier in SI-BCI as compared to SS-BCI. On the other hand, LDA accuracy was lower than accuracy achieved with SVM in both conditions (SI-BCI and SS-BCI). Our SS-BCI accuracy reached 76.85% using LDA and 94.20% using SVM and for SI-BCI we got 80.30% with LDA and 83.23% with SVM. We conclude that SI-BCI may be a feasible and relevant option, which can be used in scenarios where subjects are not able to submit themselves to long training sessions or to fast evaluation of the so called "BCI illiteracy." Comparatively, our strategy proved to be more efficient, giving us the best result for SI-BCI when faced against the classification performances of other three studies, even considering the caveat that different datasets were used in the comparison of the four studies.

Distribution Adaptation and Classification Framework Based on Multiple Kernel Learning for Motor Imagery BCI Illiteracy.

A brain-computer interface (BCI) translates a user’s thoughts such as motor imagery (MI) into the control of external devices. However, some people, who are defined as BCI illiteracy, cannot control BCI effectively. The main characteristics of BCI illiterate subjects are low classification rates and poor repeatability. To address the problem of MI-BCI illiteracy, we propose a distribution adaptation method based on multi-kernel learning to make the distribution of features between the source domain and target domain become even closer to each other, while the divisibility of categories is maximized. Inspired by the kernel trick, we adopted a multiple-kernel-based extreme learning machine to train the labeled source-domain data to find a new high-dimensional subspace that maximizes data divisibility, and then use multiple-kernel-based maximum mean discrepancy to conduct distribution adaptation to eliminate the difference in feature distribution between domains in the new subspace. In light of the high dimension of features of MI-BCI illiteracy, random forest, which can effectively handle high-dimensional features without additional cross-validation, was employed as a classifier. The proposed method was validated on an open dataset. The experimental results show that that the method we proposed suits MI-BCI illiteracy and can reduce the inter-domain differences, resulting in a reduction in the performance degradation of both cross-subjects and cross-sessions.

Sharpening Working Memory With Real-Time Electrophysiological Brain Signals: Which Neurofeedback Paradigms Work?

Growing evidence supports the idea that the ultimate biofeedback is to reward sensory pleasure (e.g., enhanced visual clarity) in real-time to neural circuits that are associated with a desired performance, such as excellent memory retrieval. Neurofeedback is biofeedback that uses real-time sensory reward to brain activity associated with a certain performance (e.g., accurate and fast recall). Working memory is a key component of human intelligence. The challenges are in our current limited understanding of neurocognitive dysfunctions as well as in technical difficulties for closed-loop feedback in true real-time. Here we review recent advancements of real time neurofeedback to improve memory training in healthy young and older adults. With new advancements in neuromarkers of specific neurophysiological functions, neurofeedback training should be better targeted beyond a single frequency approach to include frequency interactions and event-related potentials. Our review confirms the positive trend that neurofeedback training mostly works to improve memory and cognition to some extent in most studies. Yet, the training typically takes multiple weeks with 2–3 sessions per week. We review various neurofeedback reward strategies and outcome measures. A well-known issue in such training is that some people simply do not respond to neurofeedback. Thus, we also review the literature of individual differences in psychological factors e.g., placebo effects and so-called “BCI illiteracy” (Brain Computer Interface illiteracy). We recommend the use of Neural modulation sensitivity or BCI insensitivity in the neurofeedback literature. Future directions include much needed research in mild cognitive impairment, in non-Alzheimer’s dementia populations, and neurofeedback using EEG features during resting and sleep for memory enhancement and as sensitive outcome measures.

A Hybrid Brain-Computer Interface Based on Visual Evoked Potential and Pupillary Response.

Brain-computer interface (BCI) based on steady-state visual evoked potential (SSVEP) has been widely studied due to the high information transfer rate (ITR), little user training, and wide subject applicability. However, there are also disadvantages such as visual discomfort and “BCI illiteracy.” To address these problems, this study proposes to use low-frequency stimulations (12 classes, 0.8–2.12 Hz with an interval of 0.12 Hz), which can simultaneously elicit visual evoked potential (VEP) and pupillary response (PR) to construct a hybrid BCI (h-BCI) system. Classification accuracy was calculated using supervised and unsupervised methods, respectively, and the hybrid accuracy was obtained using a decision fusion method to combine the information of VEP and PR. Online experimental results from 10 subjects showed that the averaged accuracy was 94.90 ± 2.34% (data length 1.5 s) for the supervised method and 91.88 ± 3.68% (data length 4 s) for the unsupervised method, which correspond to the ITR of 64.35 ± 3.07 bits/min (bpm) and 33.19 ± 2.38 bpm, respectively. Notably, the hybrid method achieved higher accuracy and ITR than that of VEP and PR for most subjects, especially for the short data length. Together with the subjects’ feedback on user experience, these results indicate that the proposed h-BCI with the low-frequency stimulation paradigm is more comfortable and favorable than the traditional SSVEP-BCI paradigm using the alpha frequency range.

Investigating Feature Ranking Methods for Sub-Band and Relative Power Features in Motor Imagery Task Classification.

Interpreting the brain commands is now easier using brain-computer interface (BCI) technologies. Motor imagery (MI) signal detection is one of the BCI applications, where the movements of the hand and feet can be recognized via brain commands that can be further used to handle emergency situations. Design of BCI techniques encountered challenges of BCI illiteracy, poor signal to noise ratio, intersubject variability, complexity, and performance. The automated models designed for emergency should have lesser complexity and higher performance. To deal with the challenges related to the complexity performance tradeoff, the frequency features of brain signal are utilized in this study. Feature matrix is created from the power of brain frequencies, and newly proposed relative power features are used. Analysis of the relative power of alpha sub-band to beta, gamma, and theta sub-band has been done. These proposed relative features are evaluated with the help of different classifiers. For motor imagery classification, the proposed approach resulted in a maximum accuracy of 93.51% compared to other existing approaches. To check the significance of newly added features, feature ranking approaches, namely, mutual information, chi-square, and correlation, are used. The ranking of features shows that the relative power features are significant for MI task classification. The chi-square provides the best tradeoff between accuracy and feature space. We found that the addition of relative power features improves the overall performance. The proposed models could also provide quick response having reduced complexity.

Vividness of Visual Imagery and Personality Impact Motor-Imagery Brain Computer Interfaces.

Brain-computer interfaces (BCIs) are communication bridges between a human brain and external world, enabling humans to interact with their environment without muscle intervention. Their functionality, therefore, depends on both the BCI system and the cognitive capacities of the user. Motor-imagery BCIs (MI-BCI) rely on the users’ mental imagination of body movements. However, not all users have the ability to sufficiently modulate their brain activity for control of a MI-BCI; a problem known as BCI illiteracy or inefficiency. The underlying mechanism of this phenomenon and the cause of such difference among users is yet not fully understood. In this study, we investigated the impact of several cognitive and psychological measures on MI-BCI performance. Fifty-five novice BCI-users participated in a left- versus right-hand motor imagery task. In addition to their BCI classification error rate and demographics, psychological measures including personality factors, affinity for technology, and motivation during the experiment, as well as cognitive measures including visuospatial memory and spatial ability and Vividness of Visual Imagery were collected. Factors that were found to have a significant impact on MI-BCI performance were Vividness of Visual Imagery, and the personality factors of orderliness and autonomy. These findings shed light on individual traits that lead to difficulty in BCI operation and hence can help with early prediction of inefficiency among users to optimize training for them.

Alpha Frequency Intervention by Electrical Stimulation to Improve Performance in Mu-Based BCI.

The accuracy of brain-computer interfaces (BCIs) is important for effective communication and control. The mu-based BCI is one of the most widely used systems, of which the related methods to improve users' accuracy are still poorly studied, especially for the BCI illiteracy. Here, we examined a way to enhance mu-based BCI performance by electrically stimulating the ulnar nerve of the contralateral wrist at the alpha frequency (10 Hz) during left- and right-hand motor imagination in two BCI groups (literate and illiterate). We demonstrate that this alpha frequency intervention enhances the classification accuracy between left- and right-hand motor imagery from 66.41% to 81.57% immediately after intervention and to 75.28% two days after intervention in the BCI illiteracy group, while classification accuracy improves from 82.12% to 91.84% immediately after intervention and to 89.03% two days after intervention in the BCI literacy group. However, the classification accuracy did not change before and after the sham intervention (no electrical stimulation). Furthermore, the ERD on the primary sensorimotor cortex during left- or right-hand motor imagery tasks was more visible at the mu-rhythm (8-13 Hz) after alpha frequency intervention. Alpha frequency intervention increases the mu-rhythm power difference between left- and right-hand motor imagery tasks. These results provide evidence that alpha frequency intervention is an effective way to improve BCI performance by regulating the mu-rhythm which might provide a way to reduce BCI illiteracy.

Channel capacity in brain–computer interfaces

Objective. Adapted from the concept of channel capacity, the information transfer rate (ITR) has been widely used to evaluate the performance of a brain–computer interface (BCI). However, its traditional formula considers the model of a discrete memoryless channel in which the transition matrix presents very particular symmetries. As an alternative to compute the ITR, this work indicates a more general closed-form expression—also based on that channel model, but with less restrictive assumptions—and, with the aid of a selection heuristic based on a wrapper algorithm, extends such formula to detect classes that deteriorate the operation of a BCI system. Approach. The benchmark is a steady-state visually evoked potential (SSVEP)-based BCI dataset with 40 frequencies/classes, in which two scenarios are tested: (1) our proposed formula is used and the classes are gradually evaluated in the order of the class labels provided with the dataset; and (2) the same formula is used but with the classes evaluated progressively by a wrapper algorithm. In both scenarios, the canonical correlation analysis (CCA) is the tool to detect SSVEPs. Main results. Before and after class selection using this alternative ITR, the average capacity among all subjects goes from 3.71 1.68 to 4.79 0.70 bits per symbol, with p -value <0.01, and, for a supposedly BCI-illiterate subject, her/his capacity goes from 1.53 to 3.90 bits per symbol. Significance. Besides indicating a consistent formula to compute ITR, this work provides an efficient method to perform channel assessment in the context of a BCI experiment and argues that such method can be used to study BCI illiteracy.

Adaptive Time Segment Analysis for Steady-State Visual Evoked Potential Based Brain-Computer Interfaces.

The research in non-invasive Brain-Computer Interface (BCI) has led to significant improvements in the recent years for potential end users. However, the user experience and the BCI illiteracy problem remains challenging areas to address for obtaining robust and resilient clinical applications. In this study, we address the choice of the time segment for the detection of steady state visual evoked potential (SSVEP) detection. This problem has been widely addressed for the detection of event-related potentials compared to SSVEP based BCIs. The choice of this parameter is typically fixed and has a direct influence on both the detection accuracy and the information transfer rate. We propose to shift the problem of the time segment to the choice of the threshold for determining if a response has been properly detected. We consider two open-datasets for benchmarking the rationale of the approach. The results support the conclusion that an adaptive time segment for each trial, based on the selection of a threshold, can lead to a substantial higher ITR (86.92 bits/min), compared to the time segment chosen at the user (79.56 bits/min) or group level (73.78 bits/min). Finally, the results suggest that the threshold could be determined automatically in relation to the number of classes. Such an approach can leverage the literacy of SSVEP based BCI.

Successful BCI communication via high-frequency SSVEP or visual, audio or tactile P300 in 30 tested volunteers

In the pursuit to clarify the concept of "BCI illiteracy", we investigated the possibilities of attaining basic binary (yes/no) communication via brain‑computer interface (BCI). We tested four BCI paradigms: steady‑state visual evoked potentials (SSVEP), tactile, visual, and auditory evoked potentials (P300). The proposed criterion for assessing for the possibility of communication are based on the number of correct choices obtained in a given BCI paradigm after a short calibration session, without prior training. In this study users answered 20 simple "yes/no" questions. Fourteen or more correct answers rejected the null hypothesis of random choices at P=0.05. All of the 30 healthy volunteers were able to attain above‑chance choices in at least one of the four paradigms. Additionally, we tested the system in clinical settings on a patient recovering from disorders of consciousness, achieving successful communication in 2 out of 3 paradigms. In light of these facts, after a review of the sparse literature, and in the interest of motivating further research, we propose a paraphrase of de Finetti's provocative statement: "BCI illiteracy does not exist".

Adaptive multi-degree of freedom Brain Computer Interface using online feedback: Towards novel methods and metrics of mutual adaptation between humans and machines for BCI.

This paper proposes a novel adaptive online-feedback methodology for Brain Computer Interfaces (BCI). The method uses ElectroEncephaloGraphic (EEG) signals and combines motor with speech imagery to allow for tasks that involve multiple degrees of freedom (DoF). The main approach utilizes the covariance matrix descriptor as feature, and the Relevance Vector Machines (RVM) classifier. The novel contributions include, (1) a new method to select representative data to update the RVM model, and (2) an online classifier which is an adaptively-weighted mixture of RVM models to account for the users’ exploration and exploitation processes during the learning phase. Instead of evaluating the subjects’ performance solely based on the conventional metric of accuracy, we analyze their skill’s improvement based on 3 other criteria, namely the confusion matrix’s quality, the separability of the data, and their instability. After collecting calibration data for 8 minutes in the first run, 8 participants were able to control the system while receiving visual feedback in the subsequent runs. We observed significant improvement in all subjects, including two of them who fell into the BCI illiteracy category. Our proposed BCI system complements the existing approaches in several aspects. First, the co-adaptation paradigm not only adapts the classifiers, but also allows the users to actively discover their own way to use the BCI through their exploration and exploitation processes. Furthermore, the auto-calibrating system can be used immediately with a minimal calibration time. Finally, this is the first work to combine motor and speech imagery in an online feedback experiment to provide multiple DoF for BCI control applications.

Critiquing the Concept of BCI Illiteracy.

Brain-computer interfaces (BCIs) are a form of technology that read a user's neural signals to perform a task, often with the aim of inferring user intention. They demonstrate potential in a wide range of clinical, commercial, and personal applications. But BCIs are not always simple to operate, and even with training some BCI users do not operate their systems as intended. Many researchers have described this phenomenon as "BCI illiteracy," and a body of research has emerged aiming to characterize, predict, and solve this perceived problem. However, BCI illiteracy is an inadequate concept for explaining difficulty that users face in operating BCI systems. BCI illiteracy is a methodologically weak concept; furthermore, it relies on the flawed assumption that BCI users possess physiological or functional traits that prevent proficient performance during BCI use. Alternative concepts to BCI illiteracy may offer better outcomes for prospective users and may avoid the conceptual pitfalls that BCI illiteracy brings to the BCI research process.

Embodied VR environment facilitates motor imagery brain–computer interface training

Motor imagery (MI) is the predominant control paradigm for brain–computer interfaces (BCIs). After sufficient effort is invested to the training, the accuracy of commands mediated by mental imagery of bodily movements grows to a satisfactory level. However, many issues with the MI-BCIs persist; e.g., low bit transfer rate, BCI illiteracy, sub-optimal training procedure. Especially the training process for the MI-BCIs requires improvements. Currently, the training has an inappropriate form, resulting in a high mental and temporal demand on the users (weeks of training are required for the control). This study aims at addressing the issues with the MI-BCI training. To support the learning process, an embodied training environment was created. Participants were placed into a virtual reality environment observed from a first-person view of a human-like avatar, and their rehearsal of MI actions was reflected by the corresponding movements performed by the avatar. Leveraging extension of the sense of ownership, agency, and self-location towards a non-body object (principles known from the rubber hand illusion and the body transfer illusions) has already been proven to help in producing stronger EEG correlates of MI. These principles were used to facilitate the MI-BCI training process for the first time. Performance of 30 healthy participants after two sessions of training was measured using an on-line BCI scenario. The group trained using our embodied VR environment gained significantly higher average accuracy for BCI actions (58.3%) than the control group, trained with a standard MI-BCI training protocol (52.9%).

HYBRID BRAIN–COMPUTER INTERFACE PARADIGM — A STUDY

The advancements in the field of brain–computer interface (BCI) are driven by the underlying motive of improving quality of life for both healthy as well as locked in subjects. Since BCI’s are based on the response of the human brain to training or external stimuli, the improvement in terms of performance can be achieved by either enhancing the subject training procedure or by improving the external stimuli to produce maximized event related potential (ERP). P300 and steady-state visually evoked potential (SSVEP) approaches have been the most common paradigms used for stimulus-based BCI’s world over. But recently, a large number of researchers are facing a problem of BCI illiteracy in subjects, where some of the subjects showed ineffective results while training with these BCI as independent stimuli. The concept of hybrid brain–computer interface (hBCI) is a step towards eradicating this problem. Our research deals with external stimuli-based ERP generation where we discuss and compare with experimentation, three different options of visual stimulus: conventional SSVEP stimulus, P300-SSVEP hybrid stimulus, distinct target colors for P300-SSVEP-based hybrid stimulus. This paper introduces a novel hBCI paradigm and discusses the validation of improved results by comparing with the already existing stimuli options. The parameters of comparison that were considered to validate our proposal were decision accuracy (Acc), information transfer rate (ITR) and false activation rate (FAR).

Performance of Brain-Computer Interfacing Based on Tactile Selective Sensation and Motor Imagery.

A large proportion of users do not achieve adequate control using current non-invasive brain-computer interfaces (BCIs). This issue has being coined "BCI-Illiteracy" and is observed among different BCI modalities. Here, we compare the performance and the BCI-illiteracy rate of a tactile selective sensation (SS) and motor imagery (MI) BCI, for a large subject samples. We analyzed 80 experimental sessions from 57 subjects with two-class SS protocols. For SS, the group average performance was 79.8 ± 10.6%, with 43 out of the 57 subjects (75.4%) exceeding the 70% BCI-illiteracy threshold for left- and right-hand SS discrimination. When compared with previous results, this tactile BCI outperformed all other tactile BCIs currently available. We also analyzed 63 experimental sessions from 43 subjects with two-class MI BCI protocols, where the group average performance was 77.2 ± 13.3%, with 69.7% of the subjects exceeding the 70% performance threshold for left- and right-hand MI. For within-subject comparison, the 24 subjects who participated to both the SS and MI experiments, the BCI performance was superior with SS than MI especially in beta frequency band (p < 0.05), with enhanced R2 discriminative information in the somatosensory cortex for the SS modality. Both SS and MI showed a functional dissociation between lower alpha ([8 10] Hz) and upper alpha ([10 13] Hz) bands, with BCI performance significantly better in the upper alpha than the lower alpha (p < 0.05) band. In summary, we demonstrated that SS is a promising BCI modality with low BCI illiteracy issue and has great potential in practical applications reaching large population.

A Stimulus-Independent Hybrid BCI Based on Motor Imagery and Somatosensory Attentional Orientation.

Distinctive EEG signals from the motor and somatosensory cortex are generated during mental tasks of motor imagery (MI) and somatosensory attentional orientation (SAO). In this paper, we hypothesize that a combination of these two signal modalities provides improvements in a brain-computer interface (BCI) performance with respect to using the two methods separately, and generate novel types of multi-class BCI systems. Thirty two subjects were randomly divided into a Control-Group and a Hybrid-Group. In the Control-Group, the subjects performed left and right hand motor imagery (i.e., L-MI and R-MI). In the Hybrid-Group, the subjects performed the four mental tasks (i.e., L-MI, R-MI, L-SAO, and R-SAO). The results indicate that combining two of the tasks in a hybrid manner (such as L-SAO and R-MI) resulted in a significantly greater classification accuracy than when using two MI tasks. The hybrid modality reached 86.1% classification accuracy on average, with a 7.70% increase with respect to MI ( ), and 7.21% to SAO ( ) alone. Moreover, all 16 subjects in the hybrid modality reached at least 70% accuracy, which is considered the threshold for BCI illiteracy. In addition to the two-class results, the classification accuracy was 68.1% and 54.1% for the three-class and four-class hybrid BCI. Combining the induced brain signals from motor and somatosensory cortex, the proposed stimulus-independent hybrid BCI has shown improved performance with respect to individual modalities, reducing the portion of BCI-illiterate subjects, and provided novel types of multi-class BCIs.

Why standard brain-computer interface (BCI) training protocols should be changed: an experimental study

Objective. While promising, electroencephaloraphy based brain–computer interfaces (BCIs) are barely used due to their lack of reliability: 15% to 30% of users are unable to control a BCI. Standard training protocols may be partly responsible as they do not satisfy recommendations from psychology. Our main objective was to determine in practice to what extent standard training protocols impact users’ motor imagery based BCI (MI-BCI) control performance. Approach. We performed two experiments. The first consisted in evaluating the efficiency of a standard BCI training protocol for the acquisition of non-BCI related skills in a BCI-free context, which enabled us to rule out the possible impact of BCIs on the training outcome. Thus, participants (N = 54) were asked to perform simple motor tasks. The second experiment was aimed at measuring the correlations between motor tasks and MI-BCI performance. The ten best and ten worst performers of the first study were recruited for an MI-BCI experiment during which they had to learn to perform two MI tasks. We also assessed users’ spatial ability and pre-training μ rhythm amplitude, as both have been related to MI-BCI performance in the literature. Main results. Around 17% of the participants were unable to learn to perform the motor tasks, which is close to the BCI illiteracy rate. This suggests that standard training protocols are suboptimal for skill teaching. No correlation was found between motor tasks and MI-BCI performance. However, spatial ability played an important role in MI-BCI performance. In addition, once the spatial ability covariable had been controlled for, using an ANCOVA, it appeared that participants who faced difficulty during the first experiment improved during the second while the others did not. Significance. These studies suggest that (1) standard MI-BCI training protocols are suboptimal for skill teaching, (2) spatial ability is confirmed as impacting on MI-BCI performance, and (3) when faced with difficult pre-training, subjects seemed to explore more strategies and therefore learn better.

Autonomous Parameter Adjustment for SSVEP-Based BCIs with a Novel BCI Wizard.

Brain-Computer Interfaces (BCIs) transfer human brain activities into computer commands and enable a communication channel without requiring movement. Among other BCI approaches, steady-state visual evoked potential (SSVEP)-based BCIs have the potential to become accurate, assistive technologies for persons with severe disabilities. Those systems require customization of different kinds of parameters (e.g., stimulation frequencies). Calibration usually requires selecting predefined parameters by experienced/trained personnel, though in real-life scenarios an interface allowing people with no experience in programming to set up the BCI would be desirable. Another occurring problem regarding BCI performance is BCI illiteracy (also called BCI deficiency). Many articles reported that BCI control could not be achieved by a non-negligible number of users. In order to bypass those problems we developed a SSVEP-BCI wizard, a system that automatically determines user-dependent key-parameters to customize SSVEP-based BCI systems. This wizard was tested and evaluated with 61 healthy subjects. All subjects were asked to spell the phrase “RHINE WAAL UNIVERSITY” with a spelling application after key parameters were determined by the wizard. Results show that all subjects were able to control the spelling application. A mean (SD) accuracy of 97.14 (3.73)% was reached (all subjects reached an accuracy above 85% and 25 subjects even reached 100% accuracy).

The overlooked potential for social factors to improve effectiveness of brain-computer interfaces.

The surge in development of brain-computer interface (BCI) devices is highly focused on the algorithms, mechanics, and neurophysics of their production (Lebedev and Nicolelis, 2006; Lebedev et al., 2011; Opris, 2013). Here I propose that capitalizing on research findings from the field of social neuroscience can enhance training and effectiveness of BCI devices. BCIs are not just about individual brains but also about brains in interaction with other brains. Learning in a social context is more effective than non-social instruction and countless neurophysiology studies have demonstrated that social interaction actually alters physiology, including changes in neuroplasticity and arousal. Importantly, social interaction also consists of emotional responses that have powerful rewarding qualities and incur reciprocal action. Interdisciplinary cooperation between social neuroscience and BCI innovation has been proposed to promote development of more effective BCIs that demonstrate adaptability during interaction (Mattout, 2012). The challenges of BCI illiteracy, or BCI inefficiency, suggests a vital need to consider all possible contributing factors to decrease the failure rate seen in up to a third of users (Vidaurre and Blankertz, 2010). Additionally, it has been suggested that BCI inefficiency can be reduced by addressing flaws in human training approaches, which have been largely neglected (Lotte et al., 2013). Therefore, the social cues and contexts a patient has when BCIs are integrated and employed should not be overlooked for their potential to improve effectiveness.

Reinforcement learning for adaptive threshold control of restorative brain-computer interfaces: a Bayesian simulation.

Restorative brain-computer interfaces (BCI) are increasingly used to provide feedback of neuronal states in a bid to normalize pathological brain activity and achieve behavioral gains. However, patients and healthy subjects alike often show a large variability, or even inability, of brain self-regulation for BCI control, known as BCI illiteracy. Although current co-adaptive algorithms are powerful for assistive BCIs, their inherent class switching clashes with the operant conditioning goal of restorative BCIs. Moreover, due to the treatment rationale, the classifier of restorative BCIs usually has a constrained feature space, thus limiting the possibility of classifier adaptation. In this context, we applied a Bayesian model of neurofeedback and reinforcement learning for different threshold selection strategies to study the impact of threshold adaptation of a linear classifier on optimizing restorative BCIs. For each feedback iteration, we first determined the thresholds that result in minimal action entropy and maximal instructional efficiency. We then used the resulting vector for the simulation of continuous threshold adaptation. We could thus show that threshold adaptation can improve reinforcement learning, particularly in cases of BCI illiteracy. Finally, on the basis of information-theory, we provided an explanation for the achieved benefits of adaptive threshold setting.