Brain-based control of car infotainment

• URL: http://arxiv.org/abs/2004.11978v1
• Date: Fri, 24 Apr 2020 20:32:05 GMT
• Title: Brain-based control of car infotainment
• Authors: Andrea Bellotti, Sergey Antopolskiy, Anna Marchenkova, Alessia Colucciello, Pietro Avanzini, Giovanni Vecchiato, Jonas Ambeck-Madsen, Luca Ascari
• Abstract summary: We present a custom portable EEG-based Brain-Computer Interface (BCI) that exploits Event-Related Potentials (ERPs) induced with an oddball experimental paradigm to control the infotainment menu of a car. Subject-specific models were trained with different machine learning approaches to classify EEG responses to target and non-target stimuli. No statistical differences were observed between the CAs for the in-lab and in-car training sets, nor between the EEG responses in these conditions.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Nowadays, the possibility to run advanced AI on embedded systems allows natural interaction between humans and machines, especially in the automotive field. We present a custom portable EEG-based Brain-Computer Interface (BCI) that exploits Event-Related Potentials (ERPs) induced with an oddball experimental paradigm to control the infotainment menu of a car. A preliminary evaluation of the system was performed on 10 participants in a standard laboratory setting and while driving on a closed private track. The task consisted of repeated presentations of 6 different menu icons in oddball fashion. Subject-specific models were trained with different machine learning approaches on cerebral data from either only laboratory or driving experiments (in-lab and in-car models) or a combination of the two (hybrid model) to classify EEG responses to target and non-target stimuli. All models were tested on the subjects' last in-car sessions that were not used for the training. Analysis of ERPs amplitude showed statistically significant (p < 0.05) differences between the EEG responses associated with target and non-target icons, both in the laboratory and while driving. Classification Accuracy (CA) was above chance level for all subjects in all training configurations, with a deep CNN trained on the hybrid set achieving the highest scores (mean CA = 53 $\pm$ 12 %, with 16 % chance level for the 6-class discrimination). The ranking of the features importance provided by a classical BCI approach suggests an ERP-based discrimination between target and non-target responses. No statistical differences were observed between the CAs for the in-lab and in-car training sets, nor between the EEG responses in these conditions, indicating that the data collected in the standard laboratory setting could be readily used for a real driving application without a noticeable decrease in performance.

Related papers

• EEG-based AI-BCI Wheelchair Advancement: A Brain-Computer Interfacing Wheelchair System Using Machine Learning Mechanism with Right and Left Voluntary Hand Movement [0.0]
  The system is designed to simulate wheelchair navigation based on voluntary right and left-hand movements. Various machine learning models, including Support Vector Machines (SVM), XGBoost, random forest, and a Bi-directional Long Short-Term Memory (Bi-LSTM) attention-based model, were developed.
  arXiv  Detail & Related papers  (2024-10-13T07:41:37Z)
• Latent Alignment with Deep Set EEG Decoders [44.128689862889715]
  We introduce the Latent Alignment method that won the Benchmarks for EEG Transfer Learning competition. We present its formulation as a deep set applied on the set of trials from a given subject. Our experimental results show that performing statistical distribution alignment at later stages in a deep learning model is beneficial to the classification accuracy.
  arXiv  Detail & Related papers  (2023-11-29T12:40:45Z)
• Multimodal Brain-Computer Interface for In-Vehicle Driver Cognitive Load Measurement: Dataset and Baselines [20.22894459715378]
  We introduce a novel driver cognitive load assessment dataset, CL-Drive. The data was collected from 21 subjects while driving in an immersive vehicle simulator. Each driver reported their subjective cognitive load every 10 seconds throughout the experiment.
  arXiv  Detail & Related papers  (2023-04-09T16:35:31Z)
• Embedding Synthetic Off-Policy Experience for Autonomous Driving via Zero-Shot Curricula [48.58973705935691]
  We show that an agent trained using only a 10% subset of the data performs just as well as an agent trained on the entire dataset. We then demonstrate that this difficulty score can be used in a zero-shot transfer to generate curricula for an imitation-learning based planning agent.
  arXiv  Detail & Related papers  (2022-12-02T18:57:21Z)
• Neurosymbolic hybrid approach to driver collision warning [64.02492460600905]
  There are two main algorithmic approaches to autonomous driving systems. Deep learning alone has achieved state-of-the-art results in many areas. But sometimes it can be very difficult to debug if the deep learning model doesn't work.
  arXiv  Detail & Related papers  (2022-03-28T20:29:50Z)
• 2021 BEETL Competition: Advancing Transfer Learning for Subject Independence & Heterogenous EEG Data Sets [89.84774119537087]
  We design two transfer learning challenges around diagnostics and Brain-Computer-Interfacing (BCI) Task 1 is centred on medical diagnostics, addressing automatic sleep stage annotation across subjects. Task 2 is centred on Brain-Computer Interfacing (BCI), addressing motor imagery decoding across both subjects and data sets.
  arXiv  Detail & Related papers  (2022-02-14T12:12:20Z)
• EEG-based Classification of Drivers Attention using Convolutional Neural Network [0.0]
  This study compares the performance of several attention classifiers trained on participants brain activity. CNN model trained on raw EEG data obtained under kinesthetic feedback achieved the highest accuracy 89%. Our findings show that CNN and raw EEG signals can be employed for effective training of a passive BCI for real-time attention classification.
  arXiv  Detail & Related papers  (2021-08-23T10:55:52Z)
• No Fear of Heterogeneity: Classifier Calibration for Federated Learning with Non-IID Data [78.69828864672978]
  A central challenge in training classification models in the real-world federated system is learning with non-IID data. We propose a novel and simple algorithm called Virtual Representations (CCVR), which adjusts the classifier using virtual representations sampled from an approximated ssian mixture model. Experimental results demonstrate that CCVR state-of-the-art performance on popular federated learning benchmarks including CIFAR-10, CIFAR-100, and CINIC-10.
  arXiv  Detail & Related papers  (2021-06-09T12:02:29Z)
• Diverse Complexity Measures for Dataset Curation in Self-driving [80.55417232642124]
  We propose a new data selection method that exploits a diverse set of criteria that quantize interestingness of traffic scenes. Our experiments show that the proposed curation pipeline is able to select datasets that lead to better generalization and higher performance.
  arXiv  Detail & Related papers  (2021-01-16T23:45:02Z)
• AutoFIS: Automatic Feature Interaction Selection in Factorization Models for Click-Through Rate Prediction [75.16836697734995]
  We propose a two-stage algorithm called Automatic Feature Interaction Selection (AutoFIS) AutoFIS can automatically identify important feature interactions for factorization models with computational cost just equivalent to training the target model to convergence. AutoFIS has been deployed onto the training platform of Huawei App Store recommendation service.
  arXiv  Detail & Related papers  (2020-03-25T06:53:54Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.