SSVEP modulation via non-volitional neurofeedback: anproof of concept.

Neuronal oscillatory patterns are believed to underpin multiple cognitive mechanisms. Accordingly, compromised oscillatory dynamics were shown to be associated with neuropsychiatric conditions. Therefore, the possibility of modulating, or controlling, oscillatory components of brain activity as a therapeutic approach has emerged.

Single trial detection of error-related potentials in brain-machine interfaces: a survey and comparison of methods.

Error-related potential (ErrP) is a potential elicited in the brain when humans perceive an error. ErrPs have been researched in a variety of contexts, such as to increase the reliability of brain-computer interfaces (BCIs), increase the naturalness of human-machine interaction systems, teach systems, as well as study clinical conditions. Still, there is a significant challenge in detecting ErrP from a single trial, which may hamper its effective use.

A new error-monitoring brain-computer interface based on reinforcement learning for people with autism spectrum disorders.

Brain-computer interfaces (BCIs) are emerging as promising cognitive training tools in neurodevelopmental disorders, as they combine the advantages of traditional computerized interventions with real-time tailored feedback. We propose a gamified BCI based on non-volitional neurofeedback for cognitive training, aiming at reaching a neurorehabilitation tool for application in autism spectrum disorders (ASDs).The BCI consists of an emotional facial expression paradigm controlled by an intelligent agent that makes correct and wrong actions, while the user observes and judges the agent's actions.

Point Cloud Compression: Impact on Object Detection in Outdoor Contexts.

Increasing demand for more reliable and safe autonomous driving means that data involved in the various aspects of perception, such as object detection, will become more granular as the number and resolution of sensors progress. Using these data for on-the-fly object detection causes problems related to the computational complexity of onboard processing in autonomous vehicles, leading to a desire to offload computation to roadside infrastructure using vehicle-to-infrastructure communication links. The need to transmit sensor data also arises in the context of vehicle fleets exchanging sensor data, over vehicle-to-vehicle communication links.

Visuo-auditory stimuli with semantic, temporal and spatial congruence for a P300-based BCI: An exploratory test with an ALS patient in a completely locked-in state.

Background: Brain-computer interfaces (BCIs) are a promising tool for communication with completely locked-in state (CLIS) patients. Despite the great efforts already made by the BCI research community, the cases of success are still very few, very exploratory, limited in time, and based on simple 'yes/no' paradigms.

New Method: A P300-based BCI is proposed comparing two conditions, one corresponding to purely spatial auditory stimuli (AU-S) and the other corresponding to hybrid visual and spatial auditory stimuli (HVA-S).

Robust Event-Based Vision Model Estimation by Dispersion Minimisation.

We propose a novel Dispersion Minimisation framework for event-based vision model estimation, with applications to optical flow and high-speed motion estimation. The framework extends previous event-based motion compensation algorithms by avoiding computing an optimisation score based on an explicit image-based representation, which provides three main benefits: i) The framework can be extended to perform incremental estimation, i.e.

Cross-Subject Zero Calibration Driver's Drowsiness Detection: Exploring Spatiotemporal Image Encoding of EEG Signals for Convolutional Neural Network Classification.

This paper explores two methodologies for drowsiness detection using EEG signals in a sustained-attention driving task considering pre-event time windows, and focusing on cross-subject zero calibration. Driving accidents are a major cause of injuries and deaths on the road. A considerable portion of those are due to fatigue and drowsiness.

Detection of Stressful Situations Using GSR While Driving a BCI-controlled Wheelchair.

This paper analyzes the galvanic skin response (GSR) recorded from healthy and motor disabled people while steering a robotic wheelchair (RobChair ISR-UC prototype), to infer whether GSR can help in the recognition of stressful situations. Seven healthy individuals and six individuals with motor disabilities were asked to drive the RobChair by means of a brain-computer interface in indoor office environments, including complex scenarios such as passing narrow doors, avoiding obstacles, and with situations of unexpected trajectories of the wheelchair (controlled by an operator without users knowledge). All these driving situations can trigger emotional arousals such as anxiety and stress.

Double ErrP Detection for Automatic Error Correction in an ERP-Based BCI Speller.

Brain-computer interface (BCI) is a useful device for people with severe motor disabilities. However, due to its low speed and low reliability, BCI still has a very limited application in daily real-world tasks. This paper proposes a P300-based BCI speller combined with a double error-related potential (ErrP) detection to automatically correct erroneous decisions.

Pure visual imagery as a potential approach to achieve three classes of control for implementation of BCI in non-motor disorders.

Objective: The achievement of multiple instances of control with the same type of mental strategy represents a way to improve flexibility of brain-computer interface (BCI) systems. Here we test the hypothesis that pure visual motion imagery of an external actuator can be used as a tool to achieve three classes of electroencephalographic (EEG) based control, which might be useful in attention disorders.

Approach: We hypothesize that different numbers of imagined motion alternations lead to distinctive signals, as predicted by distinct motion patterns.

Importance Weighted Import Vector Machine for Unsupervised Domain Adaptation.

In real-world applications, the assumption of independent and identical distribution is no longer consistent. To alleviate the significant mismatch between source and target domains, importance weighting import vector machine, which is an adaptive classifier, is proposed. This adaptive probabilistic classification method, which is sparse and computationally efficient, can be used for unsupervised domain adaptation (DA).

Control of Brain Activity in hMT+/V5 at Three Response Levels Using fMRI-Based Neurofeedback/BCI.

A major challenge in brain-computer interface (BCI) research is to increase the number of command classes and levels of control. BCI studies often use binary control level approaches (level 0 and 1 of brain activation for each class of control). Different classes may often be achieved but not different levels of activation for the same class.

Toward a reliable gaze-independent hybrid BCI combining visual and natural auditory stimuli.

Background: Brain computer interfaces (BCIs) are one of the last communication options for patients in the locked-in state (LIS). For complete LIS patients, interfaces must be gaze-independent due to their eye impairment. However, unimodal gaze-independent approaches typically present levels of performance substantially lower than gaze-dependent approaches.

ISRUC-Sleep: A comprehensive public dataset for sleep researchers.

To facilitate the performance comparison of new methods for sleep patterns analysis, datasets with quality content, publicly-available, are very important and useful. We introduce an open-access comprehensive sleep dataset, called ISRUC-Sleep. The data were obtained from human adults, including healthy subjects, subjects with sleep disorders, and subjects under the effect of sleep medication.

Probabilistic Social Behavior Analysis by Exploring Body Motion-Based Patterns.

Understanding human behavior through nonverbal-based features, is interesting in several applications such as surveillance, ambient assisted living and human-robot interaction. In this article in order to analyze human behaviors in social context, we propose a new approach which explores interrelations between body part motions in scenarios with people doing a conversation. The novelty of this method is that we analyze body motion-based features in frequency domain to estimate different human social patterns: Interpersonal Behaviors (IBs) and a Social Role (SR).

A two-step automatic sleep stage classification method with dubious range detection.

Background: The limitations of the current systems of automatic sleep stage classification (ASSC) are essentially related to the similarities between epochs from different sleep stages and the subjects' variability. Several studies have already identified the situations with the highest likelihood of misclassification in sleep scoring. Here, we took advantage of such information to develop an ASSC system based on knowledge of subjects' variability of some indicators that characterize sleep stages and on the American Academy of Sleep Medicine (AASM) rules.

Improving the generalization capacity of cascade classifiers.

The cascade classifier is a usual approach in object detection based on vision, since it successively rejects negative occurrences, e.g., background images, in a cascade structure, keeping the processing time suitable for on-the-fly applications.

Adaptive automatic sleep stage classification under covariate shift.

Current automatic sleep stage classification (ASSC) methods that rely on polysomnographic (PSG) signals suffer from inter-subject differences that make them unreliable in facing with new and different subjects. A novel adaptive sleep scoring method based on unsupervised domain adaptation, aiming to be robust to inter-subject variability, is proposed. We assume that the sleep quality variants follow a covariate shift model, where only the sleep features distribution change in the training and test phases.

GIBS block speller: toward a gaze-independent P300-based BCI.

Brain-computer interface (BCI) opens a new communication channel for individuals with severe motor disorders. In P300-based BCIs, gazing the target event plays an important role in the BCI performance. Individuals who have their eye movements affected may lose the ability to gaze targets that are in the visual periphery.

Efficient feature selection for sleep staging based on maximal overlap discrete wavelet transform and SVM.

In this paper, a novel algorithm is proposed with application in sleep/awake detection and in multiclass sleep stage classification (awake, non rapid eye movement (NREM) sleep and REM sleep). In turn, NREM is further divided into three stages denoted here by S1, S2, and S3. Six electroencephalographic (EEG) and two electro-oculographic (EOG) channels were used in this study.

Comparison of a row-column speller vs. a novel lateral single-character speller: assessment of BCI for severe motor disabled patients.

Objective: Non-invasive brain-computer interface (BCI) based on electroencephalography (EEG) offers a new communication channel for people suffering from severe motor disorders. This paper presents a novel P300-based speller called lateral single-character (LSC). The LSC performance is compared to that of the standard row-column (RC) speller.

A minimal solution for the extrinsic calibration of a camera and a laser-rangefinder.

This paper presents a new algorithm for the extrinsic calibration of a perspective camera and an invisible 2D laser-rangefinder (LRF). The calibration is achieved by freely moving a checkerboard pattern in order to obtain plane poses in camera coordinates and depth readings in the LRF reference frame. The problem of estimating the rigid displacement between the two sensors is formulated as one of registering a set of planes and lines in the 3D space.

Statistical spatial filtering for a P300-based BCI: tests in able-bodied, and patients with cerebral palsy and amyotrophic lateral sclerosis.

The effective use of brain-computer interfaces (BCIs) in real-world environments depends on a satisfactory throughput. In a P300-based BCI, this can be attained by reducing the number of trials needed to detect the P300 signal. However, this task is hampered by the very low signal-to-noise-ratio (SNR) of P300 event related potentials.

Assisted navigation based on shared-control, using discrete and sparse human-machine interfaces.

This paper presents a shared-control approach for Assistive Mobile Robots (AMR), which depends on the user's ability to navigate a semi-autonomous powered wheelchair, using a sparse and discrete human-machine interface (HMI). This system is primarily intended to help users with severe motor disabilities that prevent them to use standard human-machine interfaces. Scanning interfaces and Brain Computer Interfaces (BCI), characterized to provide a small set of commands issued sparsely, are possible HMIs.

Novel maximum-margin training algorithms for supervised neural networks.

This paper proposes three novel training methods, two of them based on the backpropagation approach and a third one based on information theory for multilayer perceptron (MLP) binary classifiers. Both backpropagation methods are based on the maximal-margin (MM) principle. The first one, based on the gradient descent with adaptive learning rate algorithm (GDX) and named maximum-margin GDX (MMGDX), directly increases the margin of the MLP output-layer hyperplane.