Voluntary Motor Command Release Coincides with Restricted Sensorimotor Beta Rhythm Phases.

Sensory perception and memory are enhanced during restricted phases of ongoing brain rhythms, but whether voluntary movement is constrained by brain rhythm phase is not known. Voluntary movement requires motor commands to be released from motor cortex (M1) and transmitted to spinal motoneurons and effector muscles. Here, we tested the hypothesis that motor commands are preferentially released from M1 during circumscribed phases of ongoing sensorimotor rhythms.

Invariability of EEG error-related potentials during continuous feedback protocols elicited by erroneous actions at predicted or unpredicted states.

When humans perceive an erroneous action, an EEG error-related potential (ErrP) is elicited as a neural response. ErrPs have been largely investigated in discrete feedback protocols, where actions are executed at discrete steps, to enable seamless brain-computer interaction. However, there are only a few studies that investigate ErrPs in continuous feedback protocols.

Mechanisms of offline motor learning at a microscale of seconds in large-scale crowdsourced data.

Performance improvements during early human motor skill learning are suggested to be driven by short periods of rest during practice, at the scale of seconds. To reveal the unknown mechanisms behind these "micro-offline" gains, we leveraged the sampling power offered by online crowdsourcing (cumulative over all experiments = 951). First, we replicated the original in-lab findings, demonstrating generalizability to subjects learning the task in their daily living environment ( = 389).

Disentangling the origins of confidence in speeded perceptual judgments through multimodal imaging.

The human capacity to compute the likelihood that a decision is correct-known as metacognition-has proven difficult to study in isolation as it usually cooccurs with decision making. Here, we isolated postdecisional from decisional contributions to metacognition by analyzing neural correlates of confidence with multimodal imaging. Healthy volunteers reported their confidence in the accuracy of decisions they made or decisions they observed.

General principles of machine learning for brain-computer interfacing.

Brain-computer interfaces (BCIs) are systems that translate brain activity patterns into commands that can be executed by an artificial device. This enables the possibility of controlling devices such as a prosthetic arm or exoskeleton, a wheelchair, typewriting applications, or games directly by modulating our brain activity. For this purpose, BCI systems rely on signal processing and machine learning algorithms to decode the brain activity.

The Confidence Database.

Understanding how people rate their confidence is critical for the characterization of a wide range of perceptual, memory, motor and cognitive processes. To enable the continued exploration of these processes, we created a large database of confidence studies spanning a broad set of paradigms, participant populations and fields of study. The data from each study are structured in a common, easy-to-use format that can be easily imported and analysed using multiple software packages.

A Rapid Form of Offline Consolidation in Skill Learning.

The brain strengthens memories through consolidation, defined as resistance to interference (stabilization) or performance improvements between the end of a practice session and the beginning of the next (offline gains) [1]. Typically, consolidation has been measured hours or days after the completion of training [2], but the same concept may apply to periods of rest that occur interspersed in a series of practice bouts within the same session. Here, we took an unprecedented close look at the within-seconds time course of early human procedural learning over alternating short periods of practice and rest that constitute a typical online training session.

Differential contributions of subthalamic beta rhythms and 1/f broadband activity to motor symptoms in Parkinson's disease.

Excessive beta oscillatory activity in the subthalamic nucleus (STN) is linked to Parkinson's Disease (PD) motor symptoms. However, previous works have been inconsistent regarding the functional role of beta activity in untreated Parkinsonian states, questioning such role. We hypothesized that this inconsistency is due to the influence of electrophysiological broadband activity -a neurophysiological indicator of synaptic excitation/inhibition ratio- that could confound measurements of beta activity in STN recordings.

Decoding Inner Speech Using Electrocorticography: Progress and Challenges Toward a Speech Prosthesis.

Certain brain disorders resulting from brainstem infarcts, traumatic brain injury, cerebral palsy, stroke, and amyotrophic lateral sclerosis, limit verbal communication despite the patient being fully aware. People that cannot communicate due to neurological disorders would benefit from a system that can infer internal speech directly from brain signals. In this review article, we describe the state of the art in decoding inner speech, ranging from early acoustic sound features, to higher order speech units.

Sensory threshold neuromuscular electrical stimulation fosters motor imagery performance.

Motor imagery (MI) has been largely studied as a way to enhance motor learning and to restore motor functions. Although it is agreed that users should emphasize kinesthetic imagery during MI, recordings of MI brain patterns are not sufficiently reliable for many subjects. It has been suggested that the usage of somatosensory feedback would be more suitable than standardly used visual feedback to enhance MI brain patterns.

Workshops of the Sixth International Brain-Computer Interface Meeting: brain-computer interfaces past, present, and future.

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.

Word pair classification during imagined speech using direct brain recordings.

People that cannot communicate due to neurological disorders would benefit from an internal speech decoder. Here, we showed the ability to classify individual words during imagined speech from electrocorticographic signals. In a word imagery task, we used high gamma (70-150 Hz) time features with a support vector machine model to classify individual words from a pair of words.

Detecting intention to grasp during reaching movements from EEG.

Brain-computer interfaces (BCI) have been shown to be a promising tool in rehabilitation and assistive scenarios. Within these contexts, brain signals can be decoded and used as commands for a robotic device, allowing to translate user's intentions into motor actions in order to support the user's impaired neuro-muscular system. Recently, it has been suggested that slow cortical potentials (SCPs), negative deflections in the electroencephalographic (EEG) signals peaking around one second before the initiation of movements, might be of interest because they offer an accurate time resolution for the provided feedback.

Decoding fast-paced error-related potentials in monitoring protocols.

Error-related EEG potentials (ErrP) can be used for brain-machine interfacing (BMI). Decoding of these signals, indicating subject's perception of erroneous system decisions or actions can be used to correct these actions or to improve the overall interfacing system. Multiple studies have shown the feasibility of decoding these potentials in single-trial using different types of experimental protocols and feedback modalities.

Teaching brain-machine interfaces as an alternative paradigm to neuroprosthetics control.

Brain-machine interfaces (BMI) usually decode movement parameters from cortical activity to control neuroprostheses. This requires subjects to learn to modulate their brain activity to convey all necessary information, thus imposing natural limits on the complexity of tasks that can be performed. Here we demonstrate an alternative and complementary BMI paradigm that overcomes that limitation by decoding cognitive brain signals associated with monitoring processes relevant for achieving goals.

Analysis and asynchronous detection of gradually unfolding errors during monitoring tasks.

Human studies on cognitive control processes rely on tasks involving sudden-onset stimuli, which allow the analysis of these neural imprints to be time-locked and relative to the stimuli onset. Human perceptual decisions, however, comprise continuous processes where evidence accumulates until reaching a boundary. Surpassing the boundary leads to a decision where measured brain responses are associated to an internal, unknown onset.

Exploiting Task Constraints for Self-Calibrated Brain-Machine Interface Control Using Error-Related Potentials.

This paper presents a new approach for self-calibration BCI for reaching tasks using error-related potentials. The proposed method exploits task constraints to simultaneously calibrate the decoder and control the device, by using a robust likelihood function and an ad-hoc planner to cope with the large uncertainty resulting from the unknown task and decoder. The method has been evaluated in closed-loop online experiments with 8 users using a previously proposed BCI protocol for reaching tasks over a grid.

Brain connectivity in continuous error tasks.

Error-related potentials (ErrP) have been recently incorporated in brain-machine interfaces (BMIs) due to its ability to adapt and correct both the output of the BMI or the behavior of the machine. Most of these applications rely on synchronous tasks with different user's evaluations associated to correct and wrong events. Asynchronous detection during the continuous evaluation of the task, however, has to cope with background noise and an increased number of misdetections common in event-related potential detection.

Using frequency-domain features for the generalization of EEG error-related potentials among different tasks.

EEG brain-computer interfaces (BCI) require a calibration phase prior to the on-line control of the device, which is a difficulty for the practical development of this technology as it is user-, session- and task-specific. The large body of research in BCIs based on event-related potentials (ERP) use temporal features, which have demonstrated to be stable for each user along time, but do not generalize well among tasks different from the calibration task. This paper explores the use of low frequency features to improve the generalization capabilities of the BCIs using error-potentials.

Shared-control brain-computer interface for a two dimensional reaching task using EEG error-related potentials.

One of the main problems of EEG-based brain computer interfaces (BCIs) is their low information rate, thus for complex tasks the user needs large amounts of time to solve the task. In an attempt to reduce this time and improve the application robustness, recent works have explored shared-control strategies where the device does not only execute the decoded commands, but it is also involved in executing the task. This work proposes a shared-control BCI using error potentials for a 2D reaching task with discrete actions and states.

Latency correction of error potentials between different experiments reduces calibration time for single-trial classification.

One fundamental limitation of EEG-based brain-computer interfaces is the time needed to calibrate the system prior to the detection of signals, due to the wide variety of issues affecting the EEG measurements. For event-related potentials (ERP), one of these sources of variability is the application performed: Protocols with different cognitive workloads might yield to different latencies of the ERPs. In this sense, it is still not clear the effect that these latency variations have on the single-trial classification.

Minimizing calibration time using inter-subject information of single-trial recognition of error potentials in brain-computer interfaces.

One of the main problems of both synchronous and asynchronous EEG-based BCIs is the need of an initial calibration phase before the system can be used. This phase is necessary due to the high non-stationarity of the EEG, since it changes between sessions and users. The calibration process limits the BCI systems to scenarios where the outputs are very controlled, and makes these systems non-friendly and exhausting for the users.

EEG single-trial classification of visual, auditive and vibratory feedback potentials in Brain-Computer Interfaces.

Feedback stimuli are fundamental components in Brain-Computer Interfaces. It is known that the presentation of feedback stimuli elicits certain brain potentials that can be measured and classified. As stimuli can be given through different sensory modalities, it is important to understand the effects of different types of feedback on brain responses and their impact on classification.