Objective: To assess the feasibility and robustness of an asynchronous and non-invasive EEG-based Brain-Computer Interface (BCI) for continuous mental control of a wheelchair. Methods: In experiment 1 two subjects were asked to mentally drive both a real and a simulated wheelchair from a starting point to a goal along a pre-specified path. Here we only report experiments with the simulated wheelchair for which we have extensive data in a complex environment that allows a sound analysis. Each subject participated in five experimental sessions, each consisting of 10 trials. The time elapsed between two consecutive experimental sessions was variable (from 1 h to 2 months) to assess the system robustness over time. The pre-specified path was divided into seven stretches to assess the system robustness in different contexts. To further assess the performance of the brain-actuated wheelchair, subject 1 participated in a second experiment consisting of 10 trials where he was asked to drive the simulated wheelchair following 10 different complex and random paths never tried before. Results: In experiment 1 the two subjects were able to reach 100% (subject 1) and 80% (subject 2) of the final goals along the pre-specified trajectory in their best sessions. Different performances were obtained over time and path stretches, what indicates that performance is time and context dependent. In experiment 2, subject 1 was able to reach the final goal in 80% of the trials. Conclusions: The results show that subjects can rapidly master our asynchronous EEG-based BCI to control a wheelchair. Also, they can autonomously operate the BCI over long periods of time without the need for adaptive algorithms externally tuned by a human operator to minimize the impact of EEG non-stationarities. This is possible because of two key components: first, the inclusion of a shared control system between the BCI system and the intelligent simulated wheelchair; second, the selection of stable user-specific EEG features that maximize the separability between the mental tasks. Significance: These results show the feasibility of continuously controlling complex robotics devices using an asynchronous and noninvasive BCI.

This paper reviews several critical issues facing signal processing for brain–computer interfaces (BCIs) and suggests several recent approaches that should be further examined. The topics were selected based on discussions held during the 4th International BCI Meeting at a workshop organized to review and evaluate the current state of, and issues relevant to, feature extraction and translation of field potentials for BCIs. The topics presented in this paper include the relationship between electroencephalography and electrocorticography, novel features for performance prediction, time-embedded signal representations, phase information, signal non-stationarity, and unsupervised adaptation.

Abstract-In this paper we present further results of our asynchronous and non-invasive BMI for the continuous control of an intelligent wheelchair. Three subjects participated in two experiments where they steered the wheelchair spontaneously, without any external cue. To do so the users learn to voluntary modulate EEG oscillatory rhythms by executing three mental tasks (i.e., mental imagery) that are associated to different steering commands. Importantly, we implement shared control techniques between the BMI and the intelligent wheelchair to assist the subject in the driving task. The results show that the three subjects could achieve a significant level of mental control, even if far from optimal, to drive an intelligent wheelchair.

Abstract-The use of shared control techniques has a profound impact on the performance of a robotic assistant controlled by human brain signals. However, this shared control usually provides assistance to the user in a constant and identical manner each time. Creating an adaptive level of assistance, thereby complementing the user's capabilities at any moment, would be more appropriate. The better the user can do by himself, the less assistance he receives from the shared control system; and vice versa. In order to do this, we need to be able to detect when and in what way the user needs assistance. An appropriate assisting behaviour would then be activated for the time the user requires help, thereby adapting the level of assistance to the specific situation. This paper presents such a system, helping a brain-computer interface (BCI) subject perform goal-directed navigation of a simulated wheelchair in an adaptive manner. Whenever the subject has more difficulties in driving the wheelchair, more assistance will be given. Experimental results of two subjects show that this adaptive shared control increases the task performance. Also, it shows that a subject with a lower BCI performance has more need for extra assistance in difficult situations, such as manoeuvring in a narrow corridor.

What determines how we move in the world? Motor neuroscience often focusses either on intrinsic rhythmical properties of motor circuits or extrinsic sensorimotor feedback loops. Here we show that the interplay of both intrinsic and extrinsic dynamics is required to explain the intermittency observed in continuous tracking movements. Using spatiotemporal perturbations in humans, we demonstrate that apparently discrete submovements made 2–3 times per second reflect constructive interference between motor errors and continuous feedback corrections that are filtered by intrinsic circuitry in the motor system. Local field potentials in monkey motor cortex revealed characteristic signatures of a Kalman filter, giving rise to both low-frequency cortical cycles during movement, and delta oscillations during sleep. We interpret these results within the framework of optimal feedback control, and suggest that the intrinsic rhythmicity of motor cortical networks reflects an internal model of external dynamics, which is used for state estimation during feedback-guided movement.Editorial note: This article has been through an editorial process in which the authors decide how to respond to the issues raised during peer review. The Reviewing Editor's assessment is that all the issues have been addressed (<xref ref-type="decision-letter" rid="SA1">see decision letter</xref>).

Abstract. Objective: To propose a new feature extraction method with canonical solution for multi-class Brain-Computer Interfaces (BCI). The proposed method should provide a reduced number of canonical discriminant spatial patterns (CDSP) and rank the channels sorted by power discriminability (DP) between classes. Methods: The feature extractor relays in Canonical Variates Analysis (CVA) which provides the CDSP between the classes. The number of CDSP is equal to the number of classes minus one. We analyze EEG data recorded with 64 electrodes from 4 subjects recorded in 20 sessions. They were asked to execute twice in each session three different mental tasks (left hand imagination movement, rest, and words association) during 7 seconds. A ranking of electrodes sorted by power discriminability between classes and the CDSP were computed. After splitting data in training and test sets, we compared the classification accuracy achieved by Linear Discriminant Analysis (LDA) in frequency and temporal domains. Results: The average LDA classification accuracies over the four subjects using CVA on both domains are equivalent (57.89% in frequency domain and 59.43% in temporal domain). These results, in terms of classification accuracies, are also reflected in the similarity between the ranking of relevant channels in both domains. Conclusions: CVA is a simple feature extractor with canonical solution useful for multi-class BCI applications that can work on temporal or frequency domain.

Abstract-This study aims to characterize the EEG correlates of exploratory behavior. Decision making in an uncertain environment raises a conflict between two opposing needs: gathering information about the environment and exploiting this knowledge in order to optimize the decision. Exploratory behavior has already been studied using fMRI. Based on a usual paradigm in reinforcement learning, this study has shown bilateral activation in the frontal and parietal cortex. To our knowledge, no previous study has been done on it using EEG.The study of the exploratory behavior using EEG signals raises two difficulties. First, the labels of trial as exploitation or exploration cannot be directly derived from the subject action. In order to access this information, a model of how the subject makes his decision must be built. The exploration related information can be then derived from it. Second, because of the complexity of the task, its EEG correlates are not necessarily time locked with the action. So the EEG processing methods used should be designed in order to handle signals that shift in time across trials.Using the same experimental protocol as the fMRI study, results show that the bilateral frontal and parietal areas are also the most discriminant. This strongly suggests that the EEG signal also conveys information about the exploratory behavior.

A major assumption of brain–machine interface research is that patients with disconnected neural pathways can still volitionally recall precise motor commands that could be decoded for naturalistic prosthetic control. However, the disconnected condition of these patients also blocks kinaesthetic feedback from the periphery, which has been shown to regulate centrally generated output responsible for accurate motor control. Here, we tested how well motor commands are generated in the absence of kinaesthetic feedback by decoding hand movements from human scalp electroencephalography in three conditions: unimpaired movement, imagined movement, and movement attempted during temporary disconnection of peripheral afferent and efferent nerves by ischemic nerve block. Our results suggest that the recall of cortical motor commands is impoverished in the absence of kinaesthetic feedback, challenging the possibility of precise naturalistic cortical prosthetic control.