EEG synchronized left prefrontal transcranial magnetic stimulation (TMS) for treatment resistant depression is feasible and produces an entrainment dependent clinical response: A randomized controlled double blind clinical trial.

BACKGROUND: Synchronizing a TMS pulse with a person's underlying EEG rhythm can modify the brain's response. It is unclear if synchronizing rTMS trains might boost the antidepressant effect of TMS. In this first-in-human trial, we demonstrated that a single TMS pulse over the prefrontal cortex produces larger effects in the anterior cingulate depending on when it is fired relative to the individual's EEG alpha phase. OBJECTIVE/HYPOTHESES: We had three hypotheses. 1) It is feasible to synchronize repetitive TMS (rTMS) delivery to a person's preferred prefrontal alpha phase in each train of every session during a 30-visit TMS depression treatment course. 2) EEG-synchronized rTMS would produce progressive entrainment greater than unsynchronized (UNSYNC) rTMS. And 3) SYNC TMS would have better antidepressant effects than UNSYNC (remission, final Hamilton Depression Rating <10). METHODS: We enrolled (n = 34) and treated (n = 28) adults with treatment resistant depression (TRD) and randomized them to receive six weeks (30 treatments) of left prefrontal rTMS at their individual alpha frequency (IAF) (range 6-13 Hz). Prior to starting the clinical trial, all patients had an interleaved fMRI-EEG-TMS (fET) scan to determine which phase of their alpha rhythm would produce the largest BOLD response in their dorsal anterior cingulate. Our clinical EEG-rTMS system then delivered the first TMS pulse in each train time-locked to this patient-specific 'preferred phase' of each patient's left prefrontal alpha oscillation. We randomized patients (1:1) to SYNC or UNSYNC, and all were treated at their IAF. Only the SYNC patients had the first pulse of each train for all sessions synchronized to their individualized preferred alpha phase (75 trains/session ×30 sessions, 2250 synchronizations per patient over six weeks). The UNSYNC group used a random firing with respect to the alpha wave. All other TMS parameters were balanced between the two groups. The system interfaced with a MagStim Horizon air-cooled Fig. 8 TMS coil. All patients were treated at their IAF, coil in the F3 position, 120 % MT, frequency 6-13 Hz, 40 pulses per train, average 15-s inter-train interval, 3000 pulses per session. All patients, raters, and treaters were blinded. RESULTS: In the intent to treat (ITT) sample, both groups had significant clinical improvement from baseline with no significant between-group differences, with the USYNC group having mathematically more remitters but fewer responders. (ITT -15 SYNC; 13 UNSYNC, response 5 (33 %), 1 (7 %), remission 2 (13 %), 6 (46 %). The same was true with the completer sample - 12 SYNC; 12 UNSYNC, response 4, 4 (both 30 %), remission 2 (17 %), 3 (25 %)). The clinical EEG phase synchronization system performed well with no failures. The average treatment session was approximately 90 min, with 30 min for placing the EEG cap and the actual TMS treatment for 45 min (which included gathering 10 min of resting EEG). Four subjects (1 SYNC) withdrew before six weeks of treatment. All 24 completer patients were treated for six weeks despite the trial occurring during the COVID pandemic. SYNC patients exhibited increased post-stimulation EEG entrainment over the six weeks. A detailed secondary analysis of entrainment data in the SYNC group showed that responders and non-responders in this group could be cleanly separated based on the total number of sessions with entrainment and the session-to-session precision of the entrained phase. For the SYNC group only, depression improvement was greater when more sessions were entrained at similar phases. CONCLUSIONS: Synchronizing prefrontal TMS with a patient's prefrontal alpha frequency in a blinded clinical trial is possible and produces progressive EEG entrainment in synchronized patients only. There was no difference in overall clinical response in this small clinical trial. A secondary analysis showed that the consistency of the entrained phase across sessions was significantly associated with response outcome only in the SYNC group. These effects may not simply be due to how the stimulation is delivered but also whether the patient's brain can reliably entrain to a precise phase. EEG-synchronized clinical delivery of TMS is feasible and requires further study to determine the best method for determining the phase for synchronization.

Biomarkers predict the efficacy of closed-loop rTMS treatment for refractory depression.

Transcranial magnetic stimulation (TMS) is a non-invasive FDA-approved therapy for major depressive disorder (MDD), specifically for treatment-resistant depression (TRD). Though offering promise for those with TRD, its effectiveness is less than one in two patients (i.e., less than 50%). Limits on efficacy may be due to individual patient variability, but to date, there are no established biomarkers or measures of target engagement that can predict efficacy. Additionally, TMS efficacy is typically not assessed until a six-week treatment ends, precluding interim re-evaluations of the treatment. Here, we report results using a closed-loop phase-locked repetitive TMS (rTMS) treatment that synchronizes the delivery of rTMS based on the timing of the pulses relative to a patient's individual electroencephalographic (EEG) prefrontal alpha oscillation indexed by functional magnetic resonance imaging (fMRI). Among responders, synchronized rTMS produces two systematic changes in brain dynamics: a reduction in global cortical excitability and enhanced phase entrainment of cortical dynamics. These effects predict clinical outcomes in the synchronized treatment group but not in an active-treatment unsynchronized control group. The systematic decrease in excitability and increase in entrainment correlated with treatment efficacy at the endpoint and intermediate weeks during the synchronized treatment. Specifically, we show that weekly biomarker tracking enables efficacy prediction and dynamic adjustments through a treatment course, improving the overall response rates. This innovative approach advances the prospects of individualized medicine in MDD and holds potential for application in other neuropsychiatric disorders.

Pupil-linked arousal correlates with neural activity prior to sensorimotor decisions.

Objective.Sensorimotor decisions require the brain to process external information and combine it with relevant knowledge prior to actions. In this study, we explore the neural predictors of motor actions in a novel, realistic driving task designed to study decisions while driving.Approach.Through a spatiospectral assessment of functional connectivity during the premotor period, we identified the organization of visual cortex regions of interest into a distinct scene processing network. Additionally, we identified a motor action selection network characterized by coherence between the anterior cingulate cortex (ACC) and dorsolateral prefrontal cortex (DLPFC).Main results.We show that steering behavior can be predicted from oscillatory power in the visual cortex, DLPFC, and ACC. Power during the premotor periods (specific to the theta and beta bands) correlates with pupil-linked arousal and saccade duration.Significance.We interpret our findings in the context of network-level correlations with saccade-related behavior and show that the DLPFC is a key node in arousal circuitry and in sensorimotor decisions.

Increased entrainment and decreased excitability predict efficacious treatment of closed-loop phase-locked rTMS for treatment-resistant depression.

Transcranial magnetic stimulation (TMS) is an FDA-approved therapy for major depressive disorder (MDD), specifically for patients who have treatment-resistant depression (TRD). However, TMS produces response or remission in about 50% of patients but is ineffective for the other 50%. Limits on efficacy may be due to individual patient variability, but to date, there are no good biomarkers or measures of target engagement. In addition, TMS efficacy is typically not assessed until a six-week treatment ends, precluding the evaluation of intermediate improvements during the treatment duration. Here, we report on results using a closed-loop phase-locked repetitive TMS (rTMS) treatment that synchronizes the delivery of rTMS based on the timing of the pulses relative to a patient's individual electroencephalographic (EEG) prefrontal alpha oscillation informed by functional magnetic resonance imaging (fMRI). We find that, in responders, synchronized delivery of rTMS produces two systematic changes in brain dynamics. The first change is a decrease in global cortical excitability, and the second is an increase in the phase entrainment of cortical dynamics. These two effects predict clinical outcomes in the synchronized treatment group but not in an active-treatment unsynchronized control group. The systematic decrease in excitability and increase in entrainment correlated with treatment efficacy at the endpoint and intermediate weeks during the synchronized treatment. Specifically, we show that weekly tracking of these biomarkers allows for efficacy prediction and potential of dynamic adjustments through a treatment course, improving the overall response rates.

The timing of transcranial magnetic stimulation relative to the phase of prefrontal alpha EEG modulates downstream target engagement.

BACKGROUND: The communication through coherence model posits that brain rhythms are synchronized across different frequency bands and that effective connectivity strength between interacting regions depends on their phase relation. Evidence to support the model comes mostly from electrophysiological recordings in animals while evidence from human data is limited. METHODS: Here, an fMRI-EEG-TMS (fET) instrument capable of acquiring simultaneous fMRI and EEG during noninvasive single pulse TMS applied to dorsolateral prefrontal cortex (DLPFC) was used to test whether prefrontal EEG alpha phase moderates TMS-evoked top-down influences on subgenual, rostral and dorsal anterior cingulate cortex (ACC). Six runs (276 total trials) were acquired in each participant. Phase at each TMS pulse was determined post-hoc using single-trial sorting. Results were examined in two independent datasets: healthy volunteers (HV) (n = 11) and patients with major depressive disorder (MDD) (n = 17) collected as part of an ongoing clinical trial. RESULTS: In both groups, TMS-evoked functional connectivity between DLPFC and subgenual ACC (sgACC) depended on the EEG alpha phase. TMS-evoked DLPFC to sgACC fMRI-derived effective connectivity (EC) was modulated by EEG alpha phase in healthy volunteers, but not in the MDD patients. Top-down EC was inhibitory for TMS pulses during the upward slope of the alpha wave relative to TMS timed to the downward slope of the alpha wave. Prefrontal EEG alpha phase dependent effects on TMS-evoked fMRI BOLD activation of the rostral anterior cingulate cortex were detected in the MDD patient group, but not in the healthy volunteer group. DISCUSSION: Results demonstrate that TMS-evoked top-down influences vary as a function of the prefrontal alpha rhythm, and suggest potential clinical applications whereby TMS is synchronized to the brain's internal rhythms in order to more efficiently engage deep therapeutic targets.

Daily prefrontal closed-loop repetitive transcranial magnetic stimulation (rTMS) produces progressive EEG quasi-alpha phase entrainment in depressed adults.

BACKGROUND: Transcranial magnetic stimulation (TMS) is a non-invasive neuromodulation modality that can treat depression, obsessive-compulsive disorder, or help smoking cessation. Research suggests that timing the delivery of TMS relative to an endogenous brain state may affect efficacy and short-term brain dynamics. OBJECTIVE: To investigate whether, for a multi-week daily treatment of repetitive TMS (rTMS), there is an effect on brain dynamics that depends on the timing of the TMS relative to individuals' prefrontal EEG quasi-alpha rhythm (between 6 and 13 Hz). METHOD: We developed a novel closed-loop system that delivers personalized EEG-triggered rTMS to patients undergoing treatment for major depressive disorder. In a double blind study, patients received daily treatments of rTMS over a period of six weeks and were randomly assigned to either a synchronized or unsynchronized treatment group, where synchronization of rTMS was to their prefrontal EEG quasi-alpha rhythm. RESULTS: When rTMS is applied over the dorsal lateral prefrontal cortex (DLPFC) and synchronized to the patient's prefrontal quasi-alpha rhythm, patients develop strong phase entrainment over a period of weeks, both over the stimulation site as well as in a subset of areas distal to the stimulation site. In addition, at the end of the course of treatment, this group's entrainment phase shifts to be closer to the phase that optimally engages the distal target, namely the anterior cingulate cortex (ACC). These entrainment effects are not observed in the group that is given rTMS without initial EEG synchronization of each TMS train. CONCLUSIONS: The entrainment effects build over the course of days/weeks, suggesting that these effects engage neuroplastic changes which may have clinical consequences in depression or other diseases.

Spatiospectral brain networks reflective of improvisational experience.

Musical improvisers are trained to categorize certain musical structures into functional classes, which is thought to facilitate improvisation. Using a novel auditory oddball paradigm (Goldman et al., 2020) which enables us to disassociate a deviant (i.e. musical chord inversion) from a consistent functional class, we recorded scalp EEG from a group of musicians who spanned a range of improvisational and classically trained experience. Using a spatiospectral based inter and intra network connectivity analysis, we found that improvisers showed a variety of differences in connectivity within and between large-scale cortical networks compared to classically trained musicians, as a function of deviant type. Inter-network connectivity in the alpha band, for a time window leading up to the behavioural response, was strongly linked to improvisation experience, with the default mode network acting as a hub. Spatiospectral networks post response were substantially different between improvisers and classically trained musicians, with greater inter-network connectivity (specific to the alpha and beta bands) seen in improvisers whereas those with more classical training had largely reduced inter-network activity (mostly in the gamma band). More generally, we interpret our findings in the context of network-level correlates of expectation violation as a function of subject expertise, and we discuss how these may generalize to other and more ecologically valid scenarios.

Ballistocardiogram Artifact Reduction in Simultaneous EEG-fMRI Using Deep Learning.

OBJECTIVE: The concurrent recording of electroencephalography (EEG) and functional magnetic resonance imaging (fMRI) is a technique that has received much attention due to its potential for combined high temporal and spatial resolution. However, the ballistocardiogram (BCG), a large-amplitude artifact caused by cardiac induced movement contaminates the EEG during EEG-fMRI recordings. Removal of BCG in software has generally made use of linear decompositions of the corrupted EEG. This is not ideal as the BCG signal propagates in a manner which is non-linearly dependent on the electrocardiogram (ECG). In this paper, we present a novel method for BCG artifact suppression using recurrent neural networks (RNNs). METHODS: EEG signals were recovered by training RNNs on the nonlinear mappings between ECG and the BCG corrupted EEG. We evaluated our model's performance against the commonly used Optimal Basis Set (OBS) method at the level of individual subjects, and investigated generalization across subjects. RESULTS: We show that our algorithm can generate larger average power reduction of the BCG at critical frequencies, while simultaneously improving task relevant EEG based classification. CONCLUSION: The presented deep learning architecture can be used to reduce BCG related artifacts in EEG-fMRI recordings. SIGNIFICANCE: We present a deep learning approach that can be used to suppress the BCG artifact in EEG-fMRI without the use of additional hardware. This method may have scope to be combined with current hardware methods, operate in real-time and be used for direct modeling of the BCG.

Regulation of arousal via online neurofeedback improves human performance in a demanding sensory-motor task.

Our state of arousal can significantly affect our ability to make optimal decisions, judgments, and actions in real-world dynamic environments. The Yerkes-Dodson law, which posits an inverse-U relationship between arousal and task performance, suggests that there is a state of arousal that is optimal for behavioral performance in a given task. Here we show that we can use online neurofeedback to shift an individual's arousal from the right side of the Yerkes-Dodson curve to the left toward a state of improved performance. Specifically, we use a brain-computer interface (BCI) that uses information in the EEG to generate a neurofeedback signal that dynamically adjusts an individual's arousal state when they are engaged in a boundary-avoidance task (BAT). The BAT is a demanding sensory-motor task paradigm that we implement as an aerial navigation task in virtual reality and which creates cognitive conditions that escalate arousal and quickly results in task failure (e.g., missing or crashing into the boundary). We demonstrate that task performance, measured as time and distance over which the subject can navigate before failure, is significantly increased when veridical neurofeedback is provided. Simultaneous measurements of pupil dilation and heart-rate variability show that the neurofeedback indeed reduces arousal. Our work demonstrates a BCI system that uses online neurofeedback to shift arousal state and increase task performance in accordance with the Yerkes-Dodson law.

Investigating Evoked EEG Responses to Targets Presented in Virtual Reality.

Virtual reality (VR) offers the potential to study brain function in complex, ecologically realistic environments. However, the additional degrees of freedom make analysis more challenging, particularly with respect to evoked neural responses. In this paper we designed a target detection task in VR where we varied the visual angle of targets as subjects moved through a three dimensional maze. We investigated how the latency and shape of the classic P300 evoked response varied as a function of locking the electroencephalogram data to the target image onset, the target-saccade intersection, and the first fixation on the target. We found, as expected, a systematic shift in the timing of the evoked responses as a function of the type of response locking, as well as a difference in the shape of the waveforms. Interestingly, single-trial analysis showed that the peak discriminability of the evoked responses does not differ between image locked and saccade locked analysis, though it decreases significantly when fixation locked. These results suggest that there is a spread in the perception of visual information in VR environments across time and visual space. Our results point to the importance of considering how information may be perceived in naturalistic environments, specifically those that have more complexity and higher degrees of freedom than in traditional laboratory paradigms.

Compact convolutional neural networks for classification of asynchronous steady-state visual evoked potentials.

OBJECTIVE: Steady-state visual evoked potentials (SSVEPs) are neural oscillations from the parietal and occipital regions of the brain that are evoked from flickering visual stimuli. SSVEPs are robust signals measurable in the electroencephalogram (EEG) and are commonly used in brain-computer interfaces (BCIs). However, methods for high-accuracy decoding of SSVEPs usually require hand-crafted approaches that leverage domain-specific knowledge of the stimulus signals, such as specific temporal frequencies in the visual stimuli and their relative spatial arrangement. When this knowledge is unavailable, such as when SSVEP signals are acquired asynchronously, such approaches tend to fail. APPROACH: In this paper, we show how a compact convolutional neural network (Compact-CNN), which only requires raw EEG signals for automatic feature extraction, can be used to decode signals from a 12-class SSVEP dataset without the need for user-specific calibration. MAIN RESULTS: The Compact-CNN demonstrates across subject mean accuracy of approximately 80%, out-performing current state-of-the-art, hand-crafted approaches using canonical correlation analysis (CCA) and Combined-CCA. Furthermore, the Compact-CNN approach can reveal the underlying feature representation, revealing that the deep learner extracts additional phase- and amplitude-related features associated with the structure of the dataset. SIGNIFICANCE: We discuss how our Compact-CNN shows promise for BCI applications that allow users to freely gaze/attend to any stimulus at any time (e.g. asynchronous BCI) as well as provides a method for analyzing SSVEP signals in a way that might augment our understanding about the basic processing in the visual cortex.

Affective Aspects of Perceived Loss of Control and Potential Implications for Brain-Computer Interfaces.

Most brain-computer interfaces (BCIs) focus on detecting single aspects of user states (e.g., motor imagery) in the electroencephalogram (EEG) in order to use these aspects as control input for external systems. This communication can be effective, but unaccounted mental processes can interfere with signals used for classification and thereby introduce changes in the signal properties which could potentially impede BCI classification performance. To improve BCI performance, we propose deploying an approach that potentially allows to describe different mental states that could influence BCI performance. To test this approach, we analyzed neural signatures of potential affective states in data collected in a paradigm where the complex user state of perceived loss of control (LOC) was induced. In this article, source localization methods were used to identify brain dynamics with source located outside but affecting the signal of interest originating from the primary motor areas, pointing to interfering processes in the brain during natural human-machine interaction. In particular, we found affective correlates which were related to perceived LOC. We conclude that additional context information about the ongoing user state might help to improve the applicability of BCIs to real-world scenarios.

Electroencephalography Based Analysis of Working Memory Load and Affective Valence in an N-back Task with Emotional Stimuli.

Most brain-based measures of the electroencephalogram (EEG) are used in highly controlled lab environments and only focus on narrow mental states (e.g., working memory load). However, we assume that outside the lab complex multidimensional mental states are evoked. This could potentially create interference between EEG signatures used for identification of specific mental states. In this study, we aimed to investigate more realistic conditions and therefore induced a combination of working memory load and affective valence to reveal potential interferences in EEG measures. To induce changes in working memory load and affective valence, we used a paradigm which combines an N-back task (for working memory load manipulation) with a standard method to induce affect (affective pictures taken from the International Affective Picture System (IAPS) database). Subjective ratings showed that the experimental task was successful in inducing working memory load as well as affective valence. Additionally, performance measures were analyzed and it was found that behavioral performance decreased with increasing workload as well as negative valence, showing that affective valence can have an effect on cognitive processing. These findings are supported by changes in frontal theta and parietal alpha power, parameters used for measuring of working memory load in the EEG. However, these EEG measures are influenced by the negative valence condition as well and thereby show that detection of working memory load is sensitive to affective contexts. Unexpectedly, we did not find any effects for EEG measures typically used for affective valence detection (Frontal Alpha Asymmetry (FAA)). Therefore we assume that the FAA measure might not be usable if cognitive workload is induced simultaneously. We conclude that future studies should account for potential context-specifity of EEG measures.

Evaluation of Different EEG Acquisition Systems Concerning Their Suitability for Building a Brain-Computer Interface: Case Studies.

One important aspect in non-invasive brain-computer interface (BCI) research is to acquire the electroencephalogram (EEG) in a proper way. From an end-user perspective, it means with maximum comfort and without any extra inconveniences (e.g., washing the hair), whereas from a technical perspective, the signal quality has to be optimal to make the BCI work effectively and efficiently. In this work, we evaluated three different commercially available EEG acquisition systems that differ in the type of electrodes (gel-, water-, and dry-based), the amplifier technique, and the data transmission method. Every system was tested regarding three different aspects, namely, technical, BCI effectiveness and efficiency (P300 communication and control), and user satisfaction (comfort). We found that water-based system had the lowest short circuit noise level, the hydrogel-based system had the highest P300 spelling accuracies, and the dry electrode-based system caused the least inconveniences. Therefore, building a reliable BCI is possible with all the evaluated systems, and it is on the user to decide which system meets the given requirements best.

Random forests in non-invasive sensorimotor rhythm brain-computer interfaces: a practical and convenient non-linear classifier.

There is general agreement in the brain-computer interface (BCI) community that although non-linear classifiers can provide better results in some cases, linear classifiers are preferable. Particularly, as non-linear classifiers often involve a number of parameters that must be carefully chosen. However, new non-linear classifiers were developed over the last decade. One of them is the random forest (RF) classifier. Although popular in other fields of science, RFs are not common in BCI research. In this work, we address three open questions regarding RFs in sensorimotor rhythm (SMR) BCIs: parametrization, online applicability, and performance compared to regularized linear discriminant analysis (LDA). We found that the performance of RF is constant over a large range of parameter values. We demonstrate - for the first time - that RFs are applicable online in SMR-BCIs. Further, we show in an offline BCI simulation that RFs statistically significantly outperform regularized LDA by about 3%. These results confirm that RFs are practical and convenient non-linear classifiers for SMR-BCIs. Taking into account further properties of RFs, such as independence from feature distributions, maximum margin behavior, multiclass and advanced data mining capabilities, we argue that RFs should be taken into consideration for future BCIs.

Individually adapted imagery improves brain-computer interface performance in end-users with disability.

Brain-computer interfaces (BCIs) translate oscillatory electroencephalogram (EEG) patterns into action. Different mental activities modulate spontaneous EEG rhythms in various ways. Non-stationarity and inherent variability of EEG signals, however, make reliable recognition of modulated EEG patterns challenging. Able-bodied individuals who use a BCI for the first time achieve - on average - binary classification performance of about 75%. Performance in users with central nervous system (CNS) tissue damage is typically lower. User training generally enhances reliability of EEG pattern generation and thus also robustness of pattern recognition. In this study, we investigated the impact of mental tasks on binary classification performance in BCI users with central nervous system (CNS) tissue damage such as persons with stroke or spinal cord injury (SCI). Motor imagery (MI), that is the kinesthetic imagination of movement (e.g. squeezing a rubber ball with the right hand), is the "gold standard" and mainly used to modulate EEG patterns. Based on our recent results in able-bodied users, we hypothesized that pair-wise combination of "brain-teaser" (e.g. mental subtraction and mental word association) and "dynamic imagery" (e.g. hand and feet MI) tasks significantly increases classification performance of induced EEG patterns in the selected end-user group. Within-day (How stable is the classification within a day?) and between-day (How well does a model trained on day one perform on unseen data of day two?) analysis of variability of mental task pair classification in nine individuals confirmed the hypothesis. We found that the use of the classical MI task pair hand vs. feed leads to significantly lower classification accuracy - in average up to 15% less - in most users with stroke or SCI. User-specific selection of task pairs was again essential to enhance performance. We expect that the gained evidence will significantly contribute to make imagery-based BCI technology become accessible to a larger population of users including individuals with special needs due to CNS damage.

Control or non-control state: that is the question! An asynchronous visual P300-based BCI approach.

OBJECTIVE: Brain-computer interfaces (BCI) based on event-related potentials (ERP) were proven to be a reliable synchronous communication method. For everyday life situations, however, this synchronous mode is impractical because the system will deliver a selection even if the user is not paying attention to the stimulation. So far, research into attention-aware visual ERP-BCIs (i.e., asynchronous ERP-BCIs) has led to variable success. In this study, we investigate new approaches for detection of user engagement. APPROACH: Classifier output and frequency-domain features of electroencephalogram signals as well as the hybridization of them were used to detect the user's state. We tested their capabilities for state detection in different control scenarios on offline data from 21 healthy volunteers. MAIN RESULTS: The hybridization of classifier output and frequency-domain features outperformed the results of the single methods, and allowed building an asynchronous P300-based BCI with an average correct state detection accuracy of more than 95%. SIGNIFICANCE: Our results show that all introduced approaches for state detection in an asynchronous P300-based BCI can effectively avoid involuntary selections, and that the hybrid method is the most effective approach.

A co-adaptive sensory motor rhythms Brain-Computer Interface based on common spatial patterns and Random Forest.

Sensorimotor rhythm (SMR) based Brain-Computer Interfaces (BCI) typically require lengthy user training. This can be exhausting and fatiguing for the user as data collection may be monotonous and typically without any feedback for user motivation. Hence new ways to reduce user training and improve performance are needed. We recently introduced a two class motor imagery BCI system which continuously adapted with increasing run-time to the brain patterns of the user. The system was designed to provide visual feedback to the user after just five minutes. The aim of the current work was to improve user-specific online adaptation, which was expected to lead to higher performances. To maximize SMR discrimination, the method of filter-bank common spatial patterns (fbCSP) and Random Forest (RF) classifier were combined. In a supporting online study, all volunteers performed significantly better than chance. Overall peak accuracy of 88.6 ± 6.1 (SD) % was reached, which significantly exceeded the performance of our previous system by 13%. Therefore, we consider this system the next step towards fully auto-calibrating motor imagery BCIs.

Bring mental activity into action! An enhanced online co-adaptive brain-computer interface training protocol.

Non-stationarity and inherent variability of the noninvasive electroencephalogram (EEG) makes robust recognition of spontaneous EEG patterns challenging. Reliable modulation of EEG patterns that a BCI can robustly detect is a skill that users must learn. In this paper, we present a novel online co-adaptive BCI training paradigm. The system autonomously screens users for their ability to modulate EEG patterns in a predictive way and adapts its model parameters online. Results of a supporting study in seven first-time BCI users with disability are very encouraging. Three of 7 users achieved online accuracy > 70% for 2-class BCI control after 24 minutes of training. Online performance in 6 of 7 users was significantly higher than chance level. Online control was based on one single bipolar EEG channel. Beta band activity carried most discriminant information. Our fully automatic co-adaptive online approach allows to evaluate whether user benefit from current BCI technology within a reasonable timescale.

Brain-controlled applications using dynamic P300 speller matrices.

OBJECTIVES: Access to the world wide web and multimedia content is an important aspect of life. We present a web browser and a multimedia user interface adapted for control with a brain-computer interface (BCI) which can be used by severely motor impaired persons. METHODS: The web browser dynamically determines the most efficient P300 BCI matrix size to select the links on the current website. This enables control of the web browser with fewer commands and smaller matrices. The multimedia player was based on an existing software. Both applications were evaluated with a sample of ten healthy participants and three end-users. All participants used a visual P300 BCI with face-stimuli for control. RESULTS: The healthy participants completed the multimedia player task with 90% accuracy and the web browsing task with 85% accuracy. The end-users completed the tasks with 62% and 58% accuracy. All healthy participants and two out of three end-users reported that they felt to be in control of the system. CONCLUSIONS: In this study we presented a multimedia application and an efficient web browser implemented for control with a BCI. SIGNIFICANCE: Both applications provide access to important areas of modern information retrieval and entertainment.