Simultaneous Real-Time Detection of Motor Imagery and Error-Related Potentials for Improved BCI Accuracy

Brain-computer interfaces (BCIs), as any other interaction modality based on physiological signals and body channels (e.g., muscular activity, speech and gestures), are prone to errors in the recognition of subject's intent. An elegant approach to improve the accuracy of BCIs consists of a verification procedure directly based on the presence of error-related potentials (ErrP) in the EEG recorded right after the occurrence of an error. Two healthy volunteer subjects with little prior BCI experience participated in a real-time human-robot interaction experiment where they were asked to mentally move a cursor towards a target that can be reached within a few steps using motor imagery. These experiments confirm the previously reported presence of a new kind of ErrP. These Interaction ErrP exhibit a first sharp negative peak followed by a positive peak and a second broader negative peak ( 270,  330 and  430 ms after the feedback, respectively). The objective of the present study was to simultaneously detect erroneous responses of the interface and classifying motor imagery at the level of single trials in a real-time system. We have achieved online an average recognition rate of correct and erroneous single trials of 84.7% and 78.8%, respectively. The off-line post-analysis showed that the BCI error rate without the integration of ErrP detection is around 30% for both subjects. However, when integrating ErrP detection, the average online error rate drops to 7%, multiplying the bit rate by more than 3. These results show that it's possible to simultaneously extract in real-time useful information for mental control to operate a brain-actuated device as well as correlates of cognitive states such as error-related potentials to improve the quality of the brain-computer interaction

About the influence of friction and polydispersity on the jamming behavior of bead assemblies

We study the jamming of bead assemblies placed in a cylindrical container whose bottom is pierced with a circular hole. Their jamming behavior is quantified here by the mean critical diameter, that is the diameter of the hole for which the jamming probability is 0.5. Mean critical diameters of monodisperse assemblies are obtained numerically using Distinct Element Method and experimentally with steel beads. We obtain good agreement between numerical and experimental results. The influence of friction is then investigated. In particular, the formation of concentric bead rings is observed for low frictions. We identify this phenomenon as a boundary effect and study its influence on jamming. Relying on measures obtained from simulation runs, the mean critical diameter of bidisperse bead assemblies is finally found to depend only on the volume-average diameter of their constituting beads. We formulate this as a tentative law and validate it using bidisperse assemblies of steel beads

Biomimetic rehabilitation engineering: the importance of somatosensory feedback for brain-machine interfaces.

Brain-machine interfaces (BMIs) re-establish communication channels between the nervous system and an external device. The use of BMI technology has generated significant developments in rehabilitative medicine, promising new ways to restore lost sensory-motor functions. However and despite high-caliber basic research, only a few prototypes have successfully left the laboratory and are currently home-deployed. The failure of this laboratory-to-user transfer likely relates to the absence of BMI solutions for providing naturalistic feedback about the consequences of the BMI's actions. To overcome this limitation, nowadays cutting-edge BMI advances are guided by the principle of biomimicry; i.e. the artificial reproduction of normal neural mechanisms. Here, we focus on the importance of somatosensory feedback in BMIs devoted to reproducing movements with the goal of serving as a reference framework for future research on innovative rehabilitation procedures. First, we address the correspondence between users' needs and BMI solutions. Then, we describe the main features of invasive and non-invasive BMIs, including their degree of biomimicry and respective advantages and drawbacks. Furthermore, we explore the prevalent approaches for providing quasi-natural sensory feedback in BMI settings. Finally, we cover special situations that can promote biomimicry and we present the future directions in basic research and clinical applications. The continued incorporation of biomimetic features into the design of BMIs will surely serve to further ameliorate the realism of BMIs, as well as tremendously improve their actuation, acceptance, and use

Prospects on Brain-Machine Interfaces for Space System Control

The dream of controlling and guiding computer-based systems using human brain signals has slowly but steadily become a reality. The available technology allows real-time implementation of systems that measure neuronal activity, convert their signals, and translate their output for the purpose of controlling mechanical systems. This paper describes the state of the art of non-invasive BMIs and critically investigates both the current technological limits and the future potential that BMIs have for space applications. We present an assessment of the advantages that BMIs can provide and justify the preferred candidate concepts for space applications together with a vision of future directions for their implementation

Continuous Brain-Actuated Control of an Intelligent Wheelchair by Human EEG

The objective of this study is to assess the feasibility of controlling an asynchronous and non-invasive brain-actuated wheelchair by human EEG. Three subjects were asked to mentally drive the wheelchair to 3 target locations using 3 mental commands. These mental commands were respectively associated with the three wheelchair steering behaviors: turn left, turn right, and move forward. The subjects participated in 30 randomized trials (10 trials per target). The performance was assessed in terms of percentage of reached targets calculated in function of the distance between the final wheelchair position and the target at each trial. To assess the brain-actuated control achieved by the subjects, their performances were compared with the performance achieved by a random BCI. The subjects drove the wheelchair closer than 1 meter from the target in 20%, 37%, and 7 % of the trials, and closer than 2 meters in 37%, 53%, and 27 % of the trials, respectively. The random BCI drove it closer than 1 and 2 meters in 0 % and 13 % of the trials, respectively. The results show that the subjects could achieve a significant level of mental control, even if far from optimal, to drive an intelligent wheelchair, thus demonstrating the feasibility of continuously controlling complex robotics devices using an asynchronous and non-invasive BCI