High Frequency Bands and Estimated Local Field Potentials to Improve Single-Trial Classification of Electroencephalographic Signals

Non-invasive brain-computer interfaces are traditionally based on mu rhythms, beta rhythms, slow cortical potentials or P300 event-related potentials. However, there is mounting evidence that neural oscillations up to 200 Hz play important roles in processes such as attention, perception, motor action and conscious experience. In this preliminary study we propose to extend the investigations to the complete frequency spectrum and to compare the high frequency bands with the usual low frequencies. It appears that the 70-130 Hz band and the 170-230 Hz band performs better than the traditional 2-40 Hz band. In a second step we applied the same analysis to the estimated local field potentials from the scalp EEG. The same frequency bands show the best performances, and the use of eLFP leads to an increase of performances of ~5%.

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

Action prediction based on anticipatory brain potentials during simulated driving

Ricardo Andres Chavarriaga Lozano, Lucian Andrei Gheorghe, Zahra Khaliliardali

Institute of Physics2015