Combining Discriminant and Topographic Information in BCI: Preliminary Results on Stroke Patients

Non–Invasive Brain–Computer Interfaces (BCI) convey a great potential in the field of stroke rehabilitation, where the continuous monitoring of mental tasks execution could support the positive effects of standard therapies. In this paper we combine time-frequency analysis of EEG with the topographic analysis to identify and track task–related patterns of brain activity emerging during a single BCI session. 6 Stroke patients executed Motor Imagery of the affected and unaffected hands: EEG sites were ranked depending on their discriminant power (DP) at different time instants and the resulting discriminant periods were used as a prior to extract EEG Microstates. Results show that the combination of these two techniques can provide insights about specific motor–related processes happening at a fine grain temporal resolution. Such events, represented by EEG microstates, can be tracked and used both to quantify changes of underlying neural structures and to provide feedback to patients and therapists.

Action prediction based on anticipatory brain potentials during simulated driving

Ricardo Andres Chavarriaga Lozano, Lucian Andrei Gheorghe, Zahra Khaliliardali

Objective. The ability of an automobile to infer the driver's upcoming actions directly from neural signals could enrich the interaction of the car with its driver. Intelligent vehicles fitted with an on-board brain–computer interface able to decode the driver's intentions can use this information to improve the driving experience. In this study we investigate the neural signatures of anticipation of specific actions, namely braking and accelerating. Approach. We investigated anticipatory slow cortical potentials in electroencephalogram recorded from 18 healthy participants in a driving simulator using a variant of the contingent negative variation (CNV) paradigm with Go and No-go conditions: count-down numbers followed by 'Start'/'Stop' cue. We report decoding performance before the action onset using a quadratic discriminant analysis classifier based on temporal features. Main results. (i) Despite the visual and driving related cognitive distractions, we show the presence of anticipatory event related potentials locked to the stimuli onset similar to the widely reported CNV signal (with an average peak value of −8 μV at electrode Cz). (ii) We demonstrate the discrimination between cases requiring to perform an action upon imperative subsequent stimulus (Go condition, e.g. a 'Red' traffic light) versus events that do not require such action (No-go condition; e.g. a 'Yellow' light); with an average single trial classification performance of 0.83 ± 0.13 for braking and 0.79 ± 0.12 for accelerating (area under the curve). (iii) We show that the centro-medial anticipatory potentials are observed as early as 320 ± 200 ms before the action with a detection rate of 0.77 ± 0.12 in offline analysis. Significance. We show for the first time the feasibility of predicting the driver's intention through decoding anticipatory related potentials during simulated car driving with high recognition rates.

Institute of Physics2015

EEG Microstates for BCI Therapist Feedback: Preliminary Results on a Stroke Patient

Andrea Biasiucci, Ricardo Andres Chavarriaga Lozano, Robert Leeb, Hesam Sagha

Brain-Computer Interfaces convey a great potential in the field of stroke rehabilitation, where the continuous monitoring of the execution of mental tasks could support the positive effects of the therapy by reinforcing specific mental patterns. We propose the use of EEG Microstates as the building blocks of a novel BCI for Operators, and we show that preliminary results on a stroke patient seem to confirm that topographic information can provide hints on salient activity patterns on the affected hemisphere not recognizable within a simple discriminant framework. Furthermore, the technique could easily keep track of the evolution of these maps over different rehabilitation sessions, thus providing a strong hint of plasticity phenomena and cortical reorganization.

2011

Motor execution detection based on autonomic nervous system responses

Marie-Christine Fluet, Roger Gassert, Olivier Lambercy, Martin Wolf, Raphaël Zimmermann

Triggered assistance has been shown to be a successful robotic strategy for provoking motor plasticity, probably because it requires neurologic patients’ active participation to initiate a movement involving their impaired limb. Triggered assistance, however, requires sufficient residual motor control to activate the trigger and, thus, is not applicable to individuals with severe neurologic injuries. In these situations, brain and body–computer interfaces have emerged as promising solutions to control robotic devices. In this paper, we investigate the feasibility of a body–machine interface to detect motion execution only monitoring the autonomic nervous system (ANS) response. Four physiological signals were measured (blood pressure, breathing rate, skin conductance response and heart rate) during an isometric pinching task and used to train a classifier based on hidden Markov models. We performed an experiment with six healthy subjects to test the effectiveness of the classifier to detect rest and active pinching periods. The results showed that the movement execution can be accurately classified based only on peripheral autonomic signals, with an accuracy level of 84.5%, sensitivity of 83.8% and specificity of 85.2%. These results are encouraging to perform further research on the use of the ANS response in body–machine interfaces.

2013

Action prediction based on anticipatory brain potentials during simulated driving

Ricardo Andres Chavarriaga Lozano, Lucian Andrei Gheorghe, Zahra Khaliliardali

Institute of Physics2015