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Online coadaptive training has been successfully employed to enable people to control motor imagery (MI)-based brain-computer interfaces (BCIs), allowing to completely skip the lengthy and demotivating open-loop calibration stage traditionally applied before closed-loop control. However, practical reasons may often dictate to eventually switch off decoder adaptation and proceed with BCI control under a fixed BCI model, a situation that remains rather unexplored. This work studies the existence and magnitude of potential post-adaptation effects on system performance, subject learning and brain signal modulation stability in a state-of-the-art, coadaptive training regime inspired by a game-like design. The results extracted in a cohort of 20 able-bodied individuals reveal that ceasing classifier adaptation after three runs (approx. 30 min) of a single-session training protocol had no significant impact on any of the examined BCI control and learning aspects in the remaining two runs (about 20 min) with a fixed classifier. Fifteen individuals achieved accuracies that are better than chance level and allowed them to successfully execute the given task. These findings alleviate a major concern regarding the applicability of coadaptive MI BCI training, thus helping to further establish this training approach and allow full exploitation of its benefits.

Alzheimer's disease (AD) is a neurodegenerative condition driven by multifactorial etiology. Mild cognitive impairment (MCI) is a transitional condition between healthy aging and dementia. No reliable biomarkers are available to predict the conversion from MCI to AD.To evaluate the use of machine learning (ML) on a wealth of data offered by the Alzheimer's Disease Neuroimaging Initiative (ADNI) and Alzheimer's Disease Metabolomics Consortium (ADMC) database in the prediction of the MCI to AD conversion.We implemented an ML-based Random Forest (RF) algorithm to predict conversion from MCI to AD. Data related to the study population (587 MCI subjects) were analyzed by RF as separate or combined features and assessed for classification power. Four classes of variables were considered: neuropsychological test scores, AD-related cerebrospinal fluid (CSF) biomarkers, peripheral biomarkers, and structural magnetic resonance imaging (MRI) variables.The ML-based algorithm exhibited 86% accuracy in predicting the AD conversion of MCI subjects. When assessing the features that helped the most, neuropsychological test scores, MRI data, and CSF biomarkers were the most relevant in the MCI to AD prediction. Peripheral parameters were effective when employed in association with neuropsychological test scores. Age and sex differences modulated the prediction accuracy. AD conversion was more effectively predicted in females and younger subjects.Our findings support the notion that AD-related neurodegenerative processes result from the concerted activity of multiple pathological mechanisms and factors that act inside and outside the brain and are dynamically affected by age and sex.

Alzheimer’s disease (AD) is a neurodegenerative condition driven by a multifactorial etiology. We employed a machine learning (ML) based algorithm and the wealth of information offered by the Alzheimer’s Disease Neuroimaging Initiative (ADNI) database to investigate the relative contribution of clinically relevant factors for identifying subjects affected by Mild Cognitive Impairment (MCI), a transitional status between healthy aging and dementia. Our ML-based Random Forest (RF) algorithm did not help predict clinical outcomes and the AD conversion of MCI subjects. On the other hand, non-converting (ncMCI) subjects were correctly classified and predicted. Two neuropsychological tests, the FAQ and ADAS13, were the most relevant features used for the classification and prediction of younger, under 70, ncMCI subjects. Structural MRI data combined with systemic parameters and the cardiovascular status were instead the most critical factors for the classification of over 70 ncMCI subjects. Our results support the notion that AD is not an organ-specific condition and results from pathological processes inside and outside the Central Nervous System.

Virtual conference; International audience; When persons interact with the environment and experience or witness an error (e.g. an unexpected event), a specific brain pattern, known as error-related potential (ErrP) can be observed in the electroencephalographic signals (EEG). Virtual Reality (VR) technology enables users to interact with computer-generated simulated environments and to provide multi-modal sensory feedback. Using VR systems can, however, be error-prone. In this paper, we investigate the presence of ErrPs when Virtual Reality users face 3 types of visualization errors: (Te) tracking errors when manipulating virtual objects, (Fe) feedback errors, and (Be) background anomalies. We conducted an experiment in which 15 participants were exposed to the 3 types of errors while performing a center-out pick and place task in virtual reality. The results showed that tracking errors generate error-related potentials, the other types of errors did not generate such discernible patterns. In addition, we show that it is possible to detect the ErrPs generated by tracking losses in single trial, with an accuracy of 85%. This constitutes a first step towards the automatic detection of error-related potentials in VR applications, paving the way to the design of adaptive and self-corrective VR/AR applications by exploiting information directly from the user’s brain.

Neural oscillations originate predominantly from interacting cortical neurons and consequently reflect aspects of cortical information processing. However, their functional role is not yet fully understood and their interpretation is debatable. Amplitude modulations (AMs) in alpha (8–12 Hz), beta (13–30 Hz), and high gamma (70–150 Hz) band in invasive electrocorticogram (ECoG) and non-invasive electroencephalogram (EEG) signals change with behavior. Alpha and beta band AMs are typically suppressed (desynchronized) during motor behavior, while high gamma AMs highly correlate with the behavior. These two phenomena are successfully used for functional brain mapping and brain-computer interface (BCI) applications. Recent research found movement-phase related AMs (MPA) also in high beta/low gamma (24–40 Hz) EEG rhythms. These MPAs were found by separating the suppressed AMs into sustained and dynamic components. Sustained AM components are those with frequencies that are lower than the motor behavior. Dynamic components those with frequencies higher than the behavior. In this paper, we study ECoG beta/low gamma band (12–30 Hz/30–42 Hz) AM during repetitive finger movements addressing the question whether or not MPAs can be found in ECoG beta band. Indeed, MPA in the 12–18 Hz and 18–24 Hz band were found. This additional information may lead to further improvements in ECoG-based prediction and reconstruction of motor behavior by combining high gamma AM and beta band MPA.

An affective brain-computer interface (aBCI) is a direct communication pathway between human brain and computer, via which the computer tries to recognize the affective states of its user and respond accordingly. As aBCI introduces personal affective factors into human-computer interaction, it could potentially enrich the user’s experience during the interaction. Successful emotion recognition plays a key role in such a system. The state-of-the-art aBCIs leverage machine learning techniques which consist in acquiring affective electroencephalogram (EEG) signals from the user and calibrating the classifier to the affective patterns of the user. Many studies have reported satisfactory recognition accuracy using this paradigm. However, affective neural patterns are volatile over time even for the same subject. The recognition accuracy cannot be maintained if the usage of aBCI prolongs without recalibration. Existing studies have overlooked the performance evaluation of aBCI during long-term use. In this paper, we propose SAFE—an EEG dataset for stable affective feature selection. The dataset includes multiple recording sessions spanning across several days for each subject. Multiple sessions across different days were recorded so that the long-term recognition performance of aBCI can be evaluated. Based on this dataset, we demonstrate that the recognition accuracy of aBCIs deteriorates when re-calibration is ruled out during long-term usage. Then, we propose a stable feature selection method to choose the most stable affective features, for mitigating the accuracy deterioration to a lesser extent and maximizing the aBCI performance in the long run. We invite other researchers to test the performance of their aBCI algorithms on this dataset, and especially to evaluate the long-term performance of their methods.

Affective brain-computer interface (aBCI) introduces personal affective factors into human-computer interactions, which could potentially enrich the user's experience during the interaction with a computer. However, affective neural patterns are volatile even within the same subject. To maintain satisfactory emotion recognition accuracy, the state-of-the-art aBCIs mainly tailor the classifier to the subject-of-interest and require frequent re-calibrations for the classifier. In this paper, we demonstrate that the recognition accuracy of aBCIs deteriorates when re-calibration is ruled out during the long-term usage for the same subject. Then, we propose a stable feature selection method to choose the most stable affective features, for mitigating the accuracy deterioration to a lesser extent and maximizing the aBCI performance in the long run. We validate our method on a dataset comprising six subjects' EEG data collected during two sessions per day for each subject for eight consecutive days.

Operating a non-invasive electroencephalogram (EEG) based sensorimotor rhythm brain-computer interface (BCI) is a skill that typically requires extensive training. Lately, online co-adaptive feedback training approaches achieved promising results. Does this also mean that users can have meaningful BCI-based interactions after training? To answer this question an online study was conducted with 10 naive (first time) users. The users trained to gain BCI control by playing a Whack-A-Mole game for about 30 minutes. During this time BCI parameters were adapting to the users EEG patterns. The adaptation was then stopped and users continued playing the game with the trained BCI for another 20 minutes. Eight out of the ten users were able to control the BCI and play the game. These preliminary results seem to suggest that online co-adaptation is an effective way to gain BCI control.

A fully automated and online artifact removal method for the electroencephalogram (EEG) is developed for use in brain-computer interfacing (BCI). The method (FORCe) is based upon a novel combination of wavelet decomposition, independent component analysis, and thresholding. FORCe is able to operate on a small channel set during online EEG acquisition and does not require additional signals (e.g., electrooculogram signals). Evaluation of FORCe is performed offline on EEG recorded from 13 BCI particpants with cerebral palsy (CP) and online with three healthy participants. The method outperforms the state-of the-art automated artifact removal methods Lagged Auto-Mutual Information Clustering (LAMIC) and Fully Automated Statistical Thresholding for EEG artifact Rejection (FASTER), and is able to remove a wide range of artifact types including blink, electromyogram (EMG), and electrooculogram (EOG) artifacts.

Background Upper extremity deficits are prevalent in individuals with Parkinson disease (PD). In the early stages of PD, such deficits can be subtle and challenging to document on clinical examination. Objective The purpose of this study was to use a novel force sensor system to characterize grip force modulation, including force, temporal, and movement quality parameters, during a fine motor control task in individuals with early stage PD. Design A case-control study was conducted. Methods Fourteen individuals with early stage PD were compared with a control group of 14 healthy older adults. The relationship of force modulation parameters with motor symptom severity and disease chronicity also was assessed in people with PD. Force was measured during both precision and power grasp tasks using an instrumented twist-cap device capable of rotating in either direction. Results Compared with the control group, the PD group demonstrated more movement arrests during both precision and power grasp and longer total movement times during the power grasp. These deficits persisted when a concurrent cognitive task was added, with some evidence of force control deficits in the PD group, including lower rates of force production during the precision grasp task and higher peak forces during the power grasp task. For precision grasp, a higher number of movement arrests in single- and dual-task conditions as well as longer total movement times in the dual-task condition were associated with more severe motor symptoms. Limitations The sample was small and consisted of individuals in the early stages of PD with mild motor deficits. The group with PD was predominantly male, whereas the control group was predominantly female. Conclusion The results suggest that assessing grip force modulation deficits during fine motor tasks is possible with instrumented devices, and such sensitive measures may be important for detecting and tracking change early in the progression of PD.

Play, that is, self-motivated activities for enjoyment, is a significant aspect for human development and essential to learning and skill acquisition. Games, the structured form of play, are increasingly being used in brain–computer interface (BCI) and neurofeedback (NF) applications. In BCI and NF applications, patterns of the users’ brain activation are assessed in real time and fed back to the users. When users become successful in modulating their own brain activation, improvements in behavior, cognition, or motor function follow or they are able to control external devices such as a computer, wheelchair, or neuroprosthesis. In electroencephalogram-based applications, however, a large number of users cannot attain control over their own brain signals. Current approaches to attaining control require lengthy repetitive trainings. The use of games and game-like feedback aims at keeping user motivation and engagement high over time. This chapter provides an overview of existing game-like feedback modalities and critically discusses their potential value and also possible drawbacks in BCI and NF applications.

A Brain-Computer Interface (BCI) translates patterns of brain signals such as the electroencephalogram (EEG) into messages for communication and control. In the case of endogenous systems the reliable detection of induced patterns is more challenging than the detection of the more stable and stereotypical evoked responses. In the former case specific mental activities such as motor imagery are used to encode different messages. In the latter case users have to attend sensory stimuli to evoke a characteristic response. Indeed, a large number of users who try to control endogenous BCIs do not reach sufficient level of accuracy. This fact is also known as BCI “inefficiency” or “illiteracy”. In this paper we discuss and make some conjectures, based on our knowledge and experience in BCI, on whether or not online co-adaptation of human and machine can be the solution to overcome this challenge. We point out some ingredients that might be necessary for the system to be reliable and allow the users to attain sufficient control. C. Vidaurre was supported by grant number RyC-2014-15671 of the Spanish MINECO.

This chapter is intended as a comprehensive introduction to motor imagery (MI) based brain–computer interface (BCI) systems for readers with sufficient technological background but maybe not experts of the field. First, we provide a summary of all basic aspects that are necessary to understand how MI is generated and how this information can be extracted from brain signals to design a BCI system. We enumerate the most common procedures implemented in “classical” approaches and describe the difficulties that BCI researchers face to implement sufficiently robust systems. Then, we report on open issues to improve MI-based BCI technology and summarize current trends to at least partially overcome these difficulties. In summary, we give an understandable overview of MI-based BCI systems, from basic aspects to more recent advancements.

Impairment of an individual's ability to communicate is a major hurdle for active participation in education and social life. A lot of individuals with cerebral palsy (CP) have normal intelligence, however, due to their inability to communicate, they fall behind. Non-invasive electroencephalogram (EEG) based brain-computer interfaces (BCIs) have been proposed as potential assistive devices for individuals with CP. BCIs translate brain signals directly into action. Motor activity is no longer required. However, translation of EEG signals may be unreliable and requires months of training. Moreover, individuals with CP may exhibit high levels of spontaneous and uncontrolled movement, which has a large impact on EEG signal quality and results in incorrect translations. We introduce a novel thought-based row-column scanning communication board that was developed following user-centered design principles. Key features include an automatic online artifact reduction method and an evidence accumulation procedure for decision making. The latter allows robust decision making with unreliable BCI input. Fourteen users with CP participated in a supporting online study and helped to evaluate the performance of the developed system. Users were asked to select target items with the row-column scanning communication board. The results suggest that seven among eleven remaining users performed better than chance and were consequently able to communicate by using the developed system. Three users were excluded because of insufficient EEG signal quality. These results are very encouraging and represent a good foundation for the development of real-world BCI-based communication devices for users with CP.

Spectral band power features are one of the most widely used features in the studies of electroencephalogram (EEG)-based emotion recognition. The power spectral density of EEG signals is partitioned into different bands such as delta, theta, alpha and beta band etc. Though based on neuroscientific findings, the partition of frequency bands is somewhat on an ad-hoc basis, and the definition of frequency ranges of the bands of interest can vary between studies. On the other hand, it is also arguable that one definition of power bands could perform equally well on all subjects. In this paper, we propose to use autoencoder to automatically learn from each subject the salient frequency components from power spectral density estimated as periodogram by Fast Fourier Transform (FFT). We propose a network architecture especially for EEG feature extraction, one that adopts hidden unit clustering with added pooling neuron per cluster. The classification accuracy with features extracted by our proposed method is benchmarked against that with standard power features. Experimental results show that our proposed feature extraction method achieves accuracy ranging from 44% to 59% for three-emotion classification. We also see a 4-20% accuracy improvement over standard band power features.

The Sixth International Brain–Computer Interface (BCI) Meeting was held 30 May–3 June 2016 at the Asilomar Conference Grounds, Pacific Grove, California, USA. The conference included 28 workshops covering topics in BCI and brain–machine interface research. Topics included BCI for specific populations or applications, advancing BCI research through use of specific signals or technological advances, and translational and commercial issues to bring both implanted and non-invasive BCIs to market. BCI research is growing and expanding in the breadth of its applications, the depth of knowledge it can produce, and the practical benefit it can provide both for those with physical impairments and the general public. Here we provide summaries of each workshop, illustrating the breadth and depth of BCI research and highlighting important issues and calls for action to support future research and development.

[EN] Brain-computer interfaces (BCIs) have been proposed as a potential assistive device for individuals with cerebral palsy (CP) to assist with their communication needs. However, it is unclear how well-suited BCIs are to individuals with CP. Therefore, this study aims to investigate to what extent these users are able to gain control of BCIs, This work was supported by the FP7 Framework EU Research Project ABC (No. 287774). This paper only reflects the authors views and funding agencies are not liable for any use that may be made of the information contained herein. The authors thank Petar Horki, Elisabeth Friedrich, and Christian Breitwieser for advice and assistance in the development of aspects of the BCI systems used in this work.

Objective The study aimed to improve brain–computer interface (BCI)-usability by using distinct control strategies and evaluating performance, brain activity and psychological variables on a long-term basis over several months. Methods Fourteen able-bodied users participated in 10 sessions, plus a follow-up session. Users were trained to control an EEG-based 4-class BCI with the mental tasks, word association, mental subtraction, spatial navigation, and motor imagery. Results Eight users reached mean accuracies of 61–72% and managed to control all 4 classes above chance in single-sessions. Performance and brain patterns stayed stable over 10 weeks without training. Motor imagery showed the best performance and most distinct brain patterns. Participants’ fear of incompetence decreased while the quality of their imagery and task ease increased over sessions. The evaluation of feedback differed between tasks and correlated with performance. Conclusion Users can control a real-time 4-class BCI, driven by distinct mental tasks, with stable performance over months. However, general performance was rather low for effective BCI control in daily life. Possibilities for future optimizations to increase performance are discussed. Significance The evaluation of alternatives to motor imagery, long-term BCI use, and psychological variables is important to improve usability for mental imagery-based BCIs.

Contamination of the electroencephalogram (EEG) by artifacts related to head movement is a major cause of reduced signal quality. This is a problem in both neuroscience and other uses of the EEG. To attempt to reduce the influence, on the EEG, of artifacts related to head movement, an accelerometer is placed on the head and independent component analysis is applied to attempt to separate artifacts which are statistically related to head movements. To evaluate the method, EEG and accelerometer measurements are made from 14 individuals with Cerebral palsy attempting to control a sensorimotor rhythm based brain-computer interface. Results show that the approach significantly reduces the influence of head movement related artifacts in the EEG.

International audience; Research in brain-computer interfaces has achieved impressive progress towards implementing assistive technologies for restoration or substitution of lost motor capabilities, as well as supporting technologies for able-bodied subjects. Notwithstanding this progress, effective translation of these interfaces from proof-of concept prototypes into reliable applications remains elusive. As a matter of fact, most of the current BCI systems cannot be used independently for long periods of time by their intended end-users. Multiple factors that impair achieving this goal have already been identified. However, it is not clear how do they affect the overall BCI performance or how they should be tackled. This is worsened by the publication bias where only positive results are disseminated, preventing the research community from learning from its errors. This paper is the result of a workshop held at the 6th International BCI meeting in Asilomar. We summarize here the discussion on concrete research avenues and guidelines that may help overcoming common pitfalls and make BCIs become a useful alternative communication device.

Sequencing and timing of body movements are essential to perform motoric tasks. In this study, we investigate the temporal relation between cortical oscillations and human motor behavior (i.e., rhythmic finger movements). High-density EEG recordings were used for source imaging based on individual anatomy. We separated sustained and movement phase-related EEG source amplitudes based on the actual finger movements recorded by a data glove. Sustained amplitude modulations in the contralateral hand area show decrease for α (10–12 Hz) and β (18–24 Hz), but increase for high γ (60–80 Hz) frequencies during the entire movement period. Additionally, we found movement phase-related amplitudes, which resembled the flexion and extension sequence of the fingers. Especially for faster movement cadences, movement phase-related amplitudes included high β (24–30 Hz) frequencies in prefrontal areas. Interestingly, the spectral profiles and source patterns of movement phase-related amplitudes differed from sustained activities, suggesting that they represent different frequency-specific large-scale networks. First, networks were signified by the sustained element, which statically modulate their synchrony levels during continuous movements. These networks may upregulate neuronal excitability in brain regions specific to the limb, in this study the right hand area. Second, movement phase-related networks, which modulate their synchrony in relation to the movement sequence. We suggest that these frequency-specific networks are associated with distinct functions, including top-down control, sensorimotor prediction, and integration. The separation of different large-scale networks, we applied in this work, improves the interpretation of EEG sources in relation to human motor behavior.SIGNIFICANCE STATEMENTEEG recordings provide high temporal resolution suitable to relate cortical oscillations to actual movements. Investigating EEG sources during rhythmic finger movements, we distinguish sustained from movement phase-related amplitude modulations. We separate these two EEG source elements motivated by our previous findings in gait. Here, we found two types of large-scale networks, representing the right fingers in distinction from the time sequence of the movements. These findings suggest that EEG source amplitudes reconstructed in a cortical patch are the superposition of these simultaneously present network activities. Separating these frequency-specific networks is relevant for studying function and possible dysfunction of the cortical sensorimotor system in humans as well as to provide more advanced features for brain-computer interfaces.

Navigating (familiar) environments requires spatial memory and spatial orientation. Mobile information systems (IS) have largely taken on this task and have changed human behavior. What impact has the redistribution of problem solving on human skills and knowledge? We are interested in exploring how the use of IS impacts on knowledge/ignorance by means of mobile brain-body imaging. In this paper, we introduce a novel experimental testbed developed to study spatial orientation in the context of geographic maps. Key system features include data synchronization between various devices and data sources, flexibility in designing and modeling research questions and integration of online co-adaptive brain-computer interfacing (BCI) technology. Flexibility, adaptability, scalability and modifiability of the implemented system turn the testbed into a general-purpose tool for studying NeuroIS constructs.

Long-term impairment, disability and handicap are major issues after stroke. A wide range of interventions have been developed that aim to promote motor recovery in affected persons. High-intensity and task-specific training protocols show promising results. A better understanding of brain functioning in the context of motor learning and motor control may help to further improve rehabilitation outcome. Mobile brain imaging has brought advances that led to the development of models that characterize different aspects of the cortical involvement in movement. We are interested in translating those findings into online applications and lay a basis for novel rehabilitation interventions. In this paper, we use a model of gait consisting of two parameters: The state of walking (compared to upright standing) and the dynamics of the movement, i.e. the gait cadence. To this end, we perform mobile electroencephalography (EEG) measurements combined with inverse brain imaging and time-frequency analyses optimized for online application.

Operating Brain-Computer Interfaces (BCIs) that are based on the detection of changes in oscillatory non-invasive electroencephalogram (EEG) typically involves learning. Commonly the learning process is distributed between the user (reliable EEG pattern generation) and the machine (robust EEG pattern detection). Standard training approaches, however, typically do not allow users to gain meaningful levels of control. A better understanding of brain functioning or the use of sophisticated machine learning are ways to enhance control. Rethinking training paradigms is another option. In this paper, we enhance our game-based training approach by adding competitive elements. Winning is a powerful motivator that increases user engagement of the typically boring BCI training experience. We report on a user with cerebral palsy who successfully gained BCI control and played the classical Tic-Tac-Toe game against his caregiver.

System calibration and user training are essential for operating motor imagery based brain-computer interface (BCI) systems. These steps are often unintuitive and tedious for the user, and do not necessarily lead to a satisfactory level of control. We present an Adaptive BCI framework that provides feedback after only minutes of autocalibration in a two-class BCI setup. During operation, the system recurrently reselects only one out of six predefined logarithmic bandpower features (10-13 and 16-24 Hz from Laplacian derivations over C3, Cz, and C4), specifically, the feature that exhibits maximum discriminability. The system then retrains a linear discriminant analysis classifier on all available data and updates the online paradigm with the new model. Every retraining step is preceded by an online outlier rejection. Operating the system requires no engineering knowledge other than connecting the user and starting the system. In a supporting study, ten out of twelve novice users reached a criterion level of above 70% accuracy in one to three sessions (10-80 min online time) of training, with a median accuracy of 80.2 ± 11.3% in the last session. We consider the presented system a positive first step towards fully autocalibrating motor imagery BCIs.

Motor imagery is the task most commonly used to induce changes in electroencephalographic (EEG) signals for mental imagery-based brain computer interfacing (BCI). In this study, we investigated EEG patterns that were induced by seven different mental tasks (i.e. mental rotation, word association, auditory imagery, mental subtraction, spatial navigation, imagery of familiar faces and motor imagery) and evaluated the binary classification performance. The aim was to provide a broad range of reliable and user-appropriate tasks to make individual optimization of BCI control strategies possible. Nine users participated in four sessions of multi-channel EEG recordings. Mental tasks resulting most frequently in good binary classification performance include mental subtraction, word association, motor imagery and mental rotation. Our results indicate that a combination of 'brain-teasers' - tasks that require problem specific mental work (e.g. mental subtraction, word association) - and dynamic imagery tasks (e.g. motor imagery) result in highly distinguishable brain patterns that lead to an increased performance.

Objective This study investigates the impact of a continuously presented visual feedback in the form of a grasping hand on the modulation of sensorimotor EEG rhythms during online control of a brain–computer interface (BCI). Methods Two groups of participants were trained to use left or right hand motor imagery to control a specific output signal on a computer monitor: the experimental group controlled a moving hand performing an object-related grasp (‘realistic feedback’), whereas the control group controlled a moving bar (‘abstract feedback’). Continuous feedback was realized by using the outcome of a real-time classifier which was based on EEG signals recorded from left and right central sites. Results The classification results show no difference between the two feedback groups. For both groups, ERD/ERS analysis revealed a significant larger ERD during feedback presentation compared to an initial motor imagery screening session without feedback. Increased ERD during online BCI control was particularly found for the lower alpha (8–10 Hz) and for the beta bands (16–20, 20–24 Hz). Conclusions The present study demonstrates that visual BCI feedback clearly modulates sensorimotor EEG rhythms. When the feedback provides equivalent information on both the continuous and final outcomes of mental actions, the presentation form (abstract versus realistic) does not influence the performance in a BCI, at least in initial training sessions. Significance The present results are of practical interest for classifier development and BCI use in the field of motor restoration.

BCI systems let users convert thoughts into actions that do not involve voluntary muscle movement. The systems offer a new means of communication for those with paralysis or severe neuromuscular disorders. BCI technology is a relatively new, fast-growing field of research and applications with the potential to improve the quality of life in severely disabled people. To date, several BCI prototypes exist, but most work only in a laboratory environment. Before a BCI can be used for communication and control at home, research must solve several problems. An important next step is to establish protocols for easily setting up and using BCI systems in a practical environment. Many features, such as electrode positions and frequency components, must be automatically selectable for particular motor imagery. The system must use the fewest number of recording electrodes possible, striving for the optimal single EEG channel. Finally, training time must decrease, perhaps through game-like feedback and automatic detection of artifacts, such as uncontrolled muscle activity. With these improvements, which are on the horizon, we expect to see practical BCI systems for a wide range of users and applications.