Computational intelligent brain computer interaction and its applications on driving cognition

Lin, Chin‐Teng; Ko, Li-Wei; Shen, Tzu-Kuei

doi:10.1109/mci.2009.934559

Cited by 51 publications

(25 citation statements)

References 48 publications

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“…Concluding, the current findings complement recent studies that have identified correlates of other cognitive processes in realistic driving, including drowsiness [6], [21], [22], [23], emergency braking [9], [24], error-awareness [25], anticipation of self-motivated steering [8] and braking actions [7], as well as visual attention [26]. We purport that future driving assistive systems can exploit information derived from these signals -decoded through a brain-machine interface system-, in combination with information from in-car sensors to tailor the support they provide both to the perceived conditions of the environment as well as the internal state of the driver [27].…”

Section: Discussionsupporting

confidence: 85%

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Brain Correlates of Lane Changing Reaction Time in Simulated Driving

Zhang

Chavarriaga

Gheorghe

et al. 2015

2015 IEEE International Conference on Systems, Man, and Cybernetics

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Abstract-Psychophysical studies have reported correlation between neural activity in frontal and parietal areas and subject's reaction time in simple tasks. Here we study whether similar correlates can also be identified in driver's electroencephalography (EEG) activity when they perform steering actions triggered by exogenous stimuli (e.g. obstacles along the road). We report analysis of the EEG signals of fifteen subjects while they drive in a realistic car simulator. We found that the peak latency of the event-related potentials in frontal and parietal areas significantly correlates with the onset of the steering behavior. Similarly, modulations of the power in the theta band (4-8 Hz) prior to the action also correlates with the reaction times. These results provide evidence of reliable neural markers of the driver's response variability.

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Section: Discussionsupporting

confidence: 85%

“…This activity reflects underlying cognitive processes and can potentially be exploited to improve driving assistance systems for intelligent cars [6], [7], [8]. For example, recent studies have focused on detecting anticipated and emergency braking [7], [9], steering actions [8] as well as workload and levels of attention [10].…”

Section: Introductionmentioning

confidence: 99%

Brain Correlates of Lane Changing Reaction Time in Simulated Driving

Zhang

Chavarriaga

Gheorghe

et al. 2015

2015 IEEE International Conference on Systems, Man, and Cybernetics

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show abstract

“…This information can in turn be used to improve performance in such tasks. For instance, smart cars could use it to provide tailored support for each driver [1]- [3]. Existing works in these lines have focused on detecting anticipated and emergency braking [2], [4], steering actions [3] as well as workload and levels of attention [5].…”

Section: Introductionmentioning

confidence: 99%

EEG correlates of active visual search during simulated driving: An exploratory study

Renold

Chavarriaga

Gheorghe

et al. 2014

2014 IEEE International Conference on Systems, Man, and Cybernetics (SMC)

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Abstract-Brain responses to visual stimuli can provide information about visual recognition processes. Several studies have shown stimulus-dependent modulation of the evoked neural responses after gaze shifts (i.e. eye fixation related potentials, EFRP) depending on the relevance of the fixated object. However these studies are typically performed on still images under constrained conditions. Here we extend this approach to study overt visual attention during a simulated driving task. Simultaneous analysis of eye-tracking and electroencephalography data revealed similar patterns than those previously reported. However, natural visual exploration yielded shorter fixations, which imposes constraints in the analysis of the elicited brain responses. Nevertheless, we found significant differences between EFRPs corresponding to target objects or non-object stimuli. These results suggest the possibility of decoding such information during driving, allowing better understanding of how drivers process the environmental information.

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“…Our previous work applied computational intelligence technology in BCIs (i.e., drowsy and distracted driving applications [10]) to inspire detailed investigations of practical issues in real-life applications. Novel EEG devices featuring dry electrodes facilitate and speed up electrode positioning before recording and allow subjects to move freely in operational environments.…”

Section: Discussionmentioning

confidence: 99%

“…BCIs have also been shown to be successful in a wide range of applications, such as personal authentication or identification [3], [4], assessment of emotional disorders [5], games [6], and accident prevention [7][8][9][10]. However, several technical issues in signal acquisition, signal preprocessing, feature extraction, and signal translation must be addressed to facilitate the transition of laboratory-oriented neuroscience research to practical BCI devices (see Fig.…”

Section: Introductionmentioning

confidence: 99%

EEG-Based Brain-Computer Interfaces: A Novel Neurotechnology and Computational Intelligence Method

Lin

Liu

et al. 2017

IEEE Syst. Man Cybern. Mag.

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Abstract-Significant advances in neuroscience, sensor technologies, and efficient signal processing algorithms have greatly facilitated the transition from laboratory-oriented neuroscience research to practical applications. Brain-computer interfaces (BCIs) represent major strides in translating brain signals into actionable decisions and primarily consist of hardware and software that guide the communications between users and systems. This article presents several current neurotechnologies and computational intelligence methods applied to EEG-based BCIs. In the hardware aspect, novel portable EEG devices featuring dry electrodes are introduced as substitutes for traditional BCIs with wet electrodes and its bulky size. With these advantages, these novel EEG devices can acquire real-time EEG signals for operational workplaces without requiring conductive gel/paste or scalp preparations. As for the software aspect, blind source separation, artificial neural networks, effective connectivity measurements and information fusion techniques are introduced to address the technical issues of artifact removal, rapid event-related potential detection, complex brain network description, and decision fusion, respectively. For instance, information fusion technique has been utilized to attack the individual differences problem of motor imagery applications in the real-world environment. With continuous improvements in the development of a convenient approach to record brain signals and extract knowledge regarding intentions, BCI techniques are envisioned to lead to a wide range of real-life applications in the near future.

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Computational intelligent brain computer interaction and its applications on driving cognition

Cited by 51 publications

References 48 publications

Brain Correlates of Lane Changing Reaction Time in Simulated Driving

Brain Correlates of Lane Changing Reaction Time in Simulated Driving

EEG correlates of active visual search during simulated driving: An exploratory study

EEG-Based Brain-Computer Interfaces: A Novel Neurotechnology and Computational Intelligence Method

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