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Objectives: Recent studies have started to explore the implementation of brain-computer interfaces (BCI) as part of driving assistant systems. The current study presents an EEG-based BCI that decodes error-related brain activity. Such information can be used, e.g., to predict driver's intended turning direction before reaching road intersections., Approach: We executed experiments in a car simulator (N = 22) and a real car (N = 8). While subject was driving, a directional cue was shown before reaching an intersection, and we classified the presence or not of an error-related potentials from EEG to infer whether the cued direction coincided with the subject's intention. In this protocol, the directional cue can correspond to an estimation of the driving direction provided by a driving assistance system. We analyzed ERPs elicited during normal driving and evaluated the classification performance in both offline and online tests., Results: An average classification accuracy of 0.698 ± 0.065 was obtained in offline experiments in the car simulator, while tests in the real car yielded a performance of 0.682 ± 0.059. The results were significantly higher than chance level for all cases. Online experiments led to equivalent performances in both simulated and real car driving experiments. These results support the feasibility of decoding these signals to help estimating whether the driver's intention coincides with the advice provided by the driving assistant in a real car., Significance: The study demonstrates a BCI system in real-world driving, extending the work from previous simulated studies. As far as we know, this is the first online study in real car decoding driver's error-related brain activity. Given the encouraging results, the paradigm could be further improved by using more sophisticated machine learning approaches and possibly be combined with applications in intelligent vehicles.

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.

In this article, we describe a new computational model of switching between path-planning and cue-guided navigation strategies. It is based on three main assumptions: (i) the strategies are mediated by separate memory systems that learn independently and in parallel; (ii) the learning algorithms are different in the two memory systems-the cue-guided strategy uses a temporal-difference (TD) learning rule to approach a visible goal, whereas the path-planning strategy relies on a place-cell-based graph-search algorithm to learn the location of a hidden goal; (iii) a strategy selection mechanism uses TD-learning rule to choose the most successful strategy based on past experience. We propose a novel criterion for strategy selection based on the directions of goal-oriented movements suggested by the different strategies. We show that the selection criterion based on this "common currency" is capable of choosing the best among TD-learning and planning strategies and can be used to solve navigational tasks in continuous state and action spaces. The model has been successfully applied to reproduce rat behavior in two water-maze tasks in which the two strategies were shown to interact. The model was used to analyze competitive and cooperative interactions between different strategies during these tasks as well as relative influence of different types of sensory cues.

A computational model of the hippocampal function in spatial learning is presented. A spatial representation is incrementally acquired during exploration. Visual and self-motion information is fed into a network of rate-coded neurons. A consistent and stable place code emerges by unsupervised Hebbian learning between place- and head direction cells. Based on this representation, goal-oriented navigation is learnt by applying a reward-based learning mechanism between the hippocampus and nucleus accumbens. The model, validated on a real and simulated robot, successfully localises itself by recalibrating its path integrator using visual input. A navigation map is learnt after about 20 trials, comparable to rats in the water maze. In contrast to previous works, this system processes realistic visual input. No compass is needed for localisation and the reward-based learning mechanism extends discrete navigation models to continuous space. The model reproduces experimental findings and suggests several neurophysiological and behavioural predictions in the rat.

Several studies in rats support the idea of multiple neural systems competing to select the best action for reaching a goal or food location. Locale navigation strategies, necessary for reaching invisible goals, seem to be mediated by the hippocampus and the ventral and dorsomedial striatum whereas taxon strategies, applied for approaching goals in the visual field, are believed to involve the dorsolateral striatum. A computational model of action selection is presented, in which different experts, implementing locale and taxon strategies, compete in order to select the appropriate behavior for the current task. The model was tested in a simulated robot using an experimental paradigm that dissociates the use of cue and spatial information.