Deep Feature Learning for EEG Recordings

Stober, Sebastian; Sternin, Avital; Owen, Adrian M.; Grahn, Jessica A.

doi:10.48550/arxiv.1511.04306

Cited by 20 publications

(27 citation statements)

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“…The OpenMIIR dataset does not distinguish between training and test sets, so we randomly selected 10% of the dataset to use as the test dataset. As the baseline, we tested some recently proposed approaches: the deep neural network (DNN) described in [19] and the CNN described in [21]. In addition, we made comparisons to our previous work [8], that without an attention mechanism.…”

Section: Methodsmentioning

confidence: 99%

Attention-based Transfer Learning for Brain-computer Interface

Tan

Sun

Kong

et al. 2019

ICASSP 2019 - 2019 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)

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Different functional areas of the human brain play different roles in brain activity, which has not been paid sufficient research attention in the brain-computer interface (BCI) field. This paper presents a new approach for electroencephalography (EEG) classification that applies attention-based transfer learning. Our approach considers the importance of different brain functional areas to improve the accuracy of EEG classification, and provides an additional way to automatically identify brain functional areas associated with new activities without the involvement of a medical professional. We demonstrate empirically that our approach out-performs stateof-the-art approaches in the task of EEG classification, and the results of visualization indicate that our approach can detect brain functional areas related to a certain task.

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Section: Methodsmentioning

confidence: 99%

Attention-based Transfer Learning for Brain-computer Interface

Tan

Sun

Kong

et al. 2019

ICASSP 2019 - 2019 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)

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“…Some recent studies [38,43] applied pre-training strategies on electroencephalogram (EEG) signals, which share some data property similarities to eye-tracking signals. In this study, we proposed a pre-training framework with a deep learning model that can be generalized to different eye-tracking applications.…”

Section: Related Studiesmentioning

confidence: 99%

Learning Oculomotor Behaviors from Scanpath

Nuechterlein

Barney³

et al. 2021

Proceedings of the 2021 International Conference on Multimodal Interaction

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Identifying oculomotor behaviors relevant for eye-tracking applications is a critical but often challenging task. Aiming to automatically learn and extract knowledge from existing eye-tracking data, we develop a novel method that creates rich representations of oculomotor scanpaths to facilitate the learning of downstream tasks. The proposed stimulus-agnostic Oculomotor Behavior Framework (OBF) model learns human oculomotor behaviors from unsupervised and semi-supervised tasks, including reconstruction, predictive coding, fixation identification, and contrastive learning tasks. The resultant pre-trained OBF model can be used in a variety of applications. Our pre-trained model outperforms baseline approaches and traditional scanpath methods in autism spectrum disorder and viewed-stimulus classification tasks. Ablation experiments further show our proposed method could achieve even better results with larger model sizes and more diverse eye-tracking training datasets, supporting the model's potential for future eye-tracking applications. Open source code: http://github.com/BeibinLi/OBF.

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“…In addition, many existing visual classification studies have been focusing on electroencephalography (EEG)-based visual object discriminations as we explored above. EEG signals, featuring by a high temporal resolution in comparison with other neuroimaging, are generally recorded by electrodes on the surface of the scalp, which has been applied in developing several areas of braincomputer interface (BCI) classification systems, such as pictures, music, and speech recognitions [2,29,3]. Interestingly, when human evoked by the different visual or auditory stimulus, the EEG signals, could collect diverse responses of evoked potentials [6].…”

Section: Introductionmentioning

confidence: 99%

An Evoked Potential-Guided Deep Learning Brain Representation For Visual Classification

Zheng

Cao

Bai

2020

Preprint

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The new perspective in visual classification aims to decode the feature representation of visual objects from human brain activities. Recording electroencephalogram (EEG) from the brain cortex has been seen as a prevalent approach to understand the cognition process of an image classification task. In this study, we proposed a deep learning framework guided by the visual evoked potentials, called the Event-Related Potential (ERP)-Long short-term memory (LSTM) framework, extracted by EEG signals for visual classification. In specific, we first extracted the ERP sequences from multiple EEG channels to response image stimuli-related information. Then, we trained an LSTM network to learn the feature representation space of visual objects for classification. In the experiment, 10 subjects were recorded by over 50,000 EEG trials from an image dataset with 6 categories, including a total of 72 exemplars. Our results showed that our proposed ERP-LSTM framework could achieve classification accuracies of cross-subject of 66.81% and 27.08% for categories (6 classes) and exemplars (72 classes), respectively. Our results outperformed that of using the existing visual classification frameworks, by improving classification accuracies in the range of 12.62% -53.99%. Our findings suggested that decoding visual evoked potentials from EEG signals is an effective strategy to learn discriminative brain representations for visual classification.

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Deep Feature Learning for EEG Recordings

Cited by 20 publications

References 0 publications

Attention-based Transfer Learning for Brain-computer Interface

Attention-based Transfer Learning for Brain-computer Interface

Learning Oculomotor Behaviors from Scanpath

An Evoked Potential-Guided Deep Learning Brain Representation For Visual Classification

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