The challenge of controlling an auditory BCI in the case of severe motor disability.

Background: The locked-in syndrome (LIS), due to a lesion in the pons, impedes communication. This situation can also be met after some severe brain injury or in advanced Amyotrophic Lateral Sclerosis (ALS). In the most severe condition, the persons cannot communicate at all because of a complete oculomotor paralysis (Complete LIS or CLIS).

Beta bursts question the ruling power for brain-computer interfaces.

: Current efforts to build reliable brain-computer interfaces (BCI) span multiple axes from hardware, to software, to more sophisticated experimental protocols, and personalized approaches. However, despite these abundant efforts, there is still room for significant improvement. We argue that a rather overlooked direction lies in linking BCI protocols with recent advances in fundamental neuroscience.

Time-resolved dynamic computational modeling of human EEG recordings reveals gradients of generative mechanisms for the MMN response.

Despite attempts to unify the different theoretical accounts of the mismatch negativity (MMN), there is still an ongoing debate on the neurophysiological mechanisms underlying this complex brain response. On one hand, neuronal adaptation to recurrent stimuli is able to explain many of the observed properties of the MMN, such as its sensitivity to controlled experimental parameters. On the other hand, several modeling studies reported evidence in favor of Bayesian learning models for explaining the trial-to-trial dynamics of the human MMN.

Disentangling sensory precision and prior expectation of change in autism during tactile discrimination.

Predictive coding theories suggest that core symptoms in autism spectrum disorders (ASD) may stem from atypical mechanisms of perceptual inference (i.e., inferring the hidden causes of sensations).

Diverse beta burst waveform motifs characterize movement-related cortical dynamics.

Classical analyses of induced, frequency-specific neural activity typically average band-limited power over trials. More recently, it has become widely appreciated that in individual trials, beta band activity occurs as transient bursts rather than amplitude-modulated oscillations. Most studies of beta bursts treat them as unitary, and having a stereotyped waveform.

Tangent space alignment: Transfer learning for Brain-Computer Interface.

Statistical variability of electroencephalography (EEG) between subjects and between sessions is a common problem faced in the field of Brain-Computer Interface (BCI). Such variability prevents the usage of pre-trained machine learning models and requires the use of a calibration for every new session. This paper presents a new transfer learning (TL) method that deals with this variability.

Infraclinical detection of voluntary attention in coma and post-coma patients using electrophysiology.

Objective: Early functional evaluation and prognosis of patients with disorders of consciousness is a major challenge that clinical assessments alone cannot solve. Objective measures of brain activity could help resolve this uncertainty. We used electroencephalogram at bedside to detect voluntary attention with a paradigm previously validated in healthy subjects.

Dynamics of Oddball Sound Processing: Trial-by-Trial Modeling of ECoG Signals.

Recent computational models of perception conceptualize auditory oddball responses as signatures of a (Bayesian) learning process, in line with the influential view of the mismatch negativity (MMN) as a prediction error signal. Novel MMN experimental paradigms have put an emphasis on neurophysiological effects of manipulating regularity and predictability in sound sequences. This raises the question of the contextual adaptation of the learning process itself, which on the computational side speaks to the mechanisms of gain-modulated (or precision-weighted) prediction error.

An Impending Paradigm Shift in Motor Imagery Based Brain-Computer Interfaces.

The development of reliable assistive devices for patients that suffer from motor impairments following central nervous system lesions remains a major challenge in the field of non-invasive Brain-Computer Interfaces (BCIs). These approaches are predominated by electroencephalography and rely on advanced signal processing and machine learning methods to extract neural correlates of motor activity. However, despite tremendous and still ongoing efforts, their value as effective clinical tools remains limited.

Neurocomputational Underpinnings of Expected Surprise.

Predictive coding accounts of brain functions profoundly influence current approaches to perceptual synthesis. However, a fundamental paradox has emerged, that may be very relevant for understanding hallucinations, psychosis, or cognitive inflexibility: in some situations, surprise or prediction error-related responses can decrease when predicted, and yet, they can increase when we know they are predictable. This paradox is resolved by recognizing that brain responses reflect precision-weighted prediction error.

Towards Identifying Optimal Biased Feedback for Various User States and Traits in Motor Imagery BCI.

Objective: Neural self-regulation is necessary for achieving control over brain-computer interfaces (BCIs). This can be an arduous learning process especially for motor imagery BCI. Various training methods were proposed to assist users in accomplishing BCI control and increase performance.

Towards a computational phenomenology of mental action: modelling meta-awareness and attentional control with deep parametric active inference.

Meta-awareness refers to the capacity to explicitly notice the current content of consciousness and has been identified as a key component for the successful control of cognitive states, such as the deliberate direction of attention. This paper proposes a formal model of meta-awareness and attentional control using hierarchical active inference. To do so, we cast mental action as policy selection over higher-level cognitive states and add a further hierarchical level to model meta-awareness states that modulate the expected confidence (precision) in the mapping between observations and hidden cognitive states.

Is Ultra Hypofractionated Radiation Therapy a Safe and Effective Treatment for Invasive Bladder Cancer in the Elderly?: A Retrospective Single Institution Review.

Purpose: The aim was to determine the efficacy, safety, and tolerability of weekly ultra hypofractionated radiation therapy for older unfit patients with invasive bladder cancer.

Methods: We retrospectively analyzed a cohort of patients with muscle invasive bladder cancer deemed unfit for chemoradiation therapy and thus treated with 6 weekly doses of 6 Gy using intensity modulated radiotherapy. Charlson comorbidity was calculated retrospectively.

Automatic analysis of single-channel sleep EEG in a large spectrum of sleep disorders.

Study Objectives: To assess the performance of the single-channel automatic sleep staging (AS) software ASEEGA in adult patients diagnosed with various sleep disorders.

Methods: Sleep recordings were included of 95 patients (38 women, 40.5 ± 13.

Empirical Bayes evaluation of fused EEG-MEG source reconstruction: Application to auditory mismatch evoked responses.

We here turn the general and theoretical question of the complementarity of EEG and MEG for source reconstruction, into a practical empirical one. Precisely, we address the challenge of evaluating multimodal data fusion on real data. For this purpose, we build on the flexibility of Parametric Empirical Bayes, namely for EEG-MEG data fusion, group level inference and formal hypothesis testing.

Estimates of cortical column orientation improve MEG source inversion.

Determining the anatomical source of brain activity non-invasively measured from EEG or MEG sensors is challenging. In order to simplify the source localization problem, many techniques introduce the assumption that current sources lie on the cortical surface. Another common assumption is that this current flow is orthogonal to the cortical surface, thereby approximating the orientation of cortical columns.

Exploring scoring methods for research studies: Accuracy and variability of visual and automated sleep scoring.

Sleep studies face new challenges in terms of data, objectives and metrics. This requires reappraising the adequacy of existing analysis methods, including scoring methods. Visual and automatic sleep scoring of healthy individuals were compared in terms of reliability (i.

Active inference as a unifying, generic and adaptive framework for a P300-based BCI.

Objective: Going adaptive is a major challenge for the field of brain-computer interface (BCI). This entails a machine that optimally articulates inference about the user's intentions and its own actions. Adaptation can operate over several dimensions which calls for a generic and flexible framework.

EEG artifact correction strategies for online trial-by-trial analysis.

Unlabelled: Brain-machine interfaces (BMIs) use brain signals to control closed-loop systems in real-time. This comes with substantial challenges, such as having to remove artifacts in order to extract reliable features, especially when using electroencephalography (EEG). Some approaches have been described in the literature to address online artifact correction.

EEG neurofeedback research: A fertile ground for psychiatry?

The clinical efficacy of neurofeedback is still a matter of debate. This paper analyzes the factors that should be taken into account in a transdisciplinary approach to evaluate the use of EEG NFB as a therapeutic tool in psychiatry. Neurofeedback is a neurocognitive therapy based on human-computer interaction that enables subjects to train voluntarily and modify functional biomarkers that are related to a defined mental disorder.

The epistemic and pragmatic value of non-action: a predictive coding perspective on meditation.

The surge of interest about mindfulness meditation is associated with a growing empirical evidence about its impact on the mind and body. Yet, despite promising phenomenological or psychological models of mindfulness, a general mechanistic understanding of meditation steeped in neuroscience is still lacking. In parallel, predictive processing approaches to the mind are rapidly developing in the cognitive sciences with an impressive explanatory power: processes apparently as diverse as perception, action, attention, and learning, can be seen as unfolding and being coherently orchestrated according to the single general mandate of free-energy minimization.

Tactile hypersensitivity and GABA concentration in the sensorimotor cortex of adults with autism.

Sensory hypersensitivity is frequently encountered in autism spectrum disorder (ASD). Gamma-aminobutyric acid (GABA) has been hypothesized to play a role in tactile hypersensitivity. The aim of the present study was twofold.

Correction to: Adults with Autism Tend to Underestimate the Hidden Environmental Structure: Evidence from a Visual Associative Learning Task.

The original version of this article unfortunately contained a mistake in the article title.

Adults with Autism Tend to Underestimate the Hidden Environmental Structure: Evidence from a Visual Associative Learning Task.

The learning-style theory of Autism Spectrum Disorders (ASD) (Qian, Lipkin, Frontiers in Human Neuroscience 5:77, 2011) states that ASD individuals differ from neurotypics in the way they learn and store information about the environment and its structure. ASD would rather adopt a lookup-table strategy (LUT: memorizing each experience), while neurotypics would favor an interpolation style (INT: extracting regularities to generalize). In a series of visual behavioral tasks, we tested this hypothesis in 20 neurotypical and 20 ASD adults.