Generative Adversarial Networks Conditioned by Brain Signals

Palazzo, Simone; Spampinato, Concetto; Kavasidis, Isaak; Giordano, Daniela; Shah, Mubarak

doi:10.1109/iccv.2017.369

Cited by 107 publications

(118 citation statements)

References 23 publications

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“…For studies reporting results for different architectures and number of layers, we only considered the highest value. We observed that most of the selected studies (128) utilized architectures with at most 10 layers. A total of 16 articles have not reported the architecture depth.…”

Section: Number Of Layersmentioning

confidence: 99%

Deep learning-based electroencephalography analysis: a systematic review

et al. 2019

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Context. Electroencephalography (EEG) is a complex signal and can require several years of training, as well as advanced signal processing and feature extraction methodologies to be correctly interpreted. Recently, deep learning (DL) has shown great promise in helping make sense of EEG signals due to its capacity to learn good feature representations from raw data. Whether DL truly presents advantages as compared to more traditional EEG processing approaches, however, remains an open question.Objective. In this work, we review 156 papers that apply DL to EEG, published between January 2010 and July 2018, and spanning different application domains such as epilepsy, sleep, braincomputer interfacing, and cognitive and affective monitoring. We extract trends and highlight interesting approaches from this large body of literature in order to inform future research and formulate recommendations.Methods. Major databases spanning the fields of science and engineering were queried to identify relevant studies published in scientific journals, conferences, and electronic preprint repositories. Various data items were extracted for each study pertaining to 1) the data, 2) the preprocessing methodology, 3) the DL design choices, 4) the results, and 5) the reproducibility of the experiments. These items were then analyzed one by one to uncover trends. * The first two authors contributed equally to this work.Significance. To help the community progress and share work more effectively, we provide a list of recommendations for future studies. We also make our summary table of DL and EEG papers available and invite authors of published work to contribute to it directly.

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Section: Number Of Layersmentioning

confidence: 99%

Deep learning-based electroencephalography analysis: a systematic review

et al. 2019

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“…The results show that the amplitudes-perturbation is a powerful method to improve the performance of deep learning models when training data is insufficient. Our future work will concentrate on improving the accuracy by other data augmentation methods, such as generative adversarial networks [27] and the variational autoencoder [28].…”

Section: Discussionmentioning

confidence: 99%

An Amplitudes-Perturbation Data Augmentation Method in Convolutional Neural Networks for EEG Decoding

Zhang

Lei

Yang

2018

2018 5th International Conference on Information, Cybernetics, and Computational Social Systems (ICCSS)

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Brain-Computer Interface (BCI) system provides a pathway between humans and the outside world by analyzing brain signals which contain potential neural information. Electroencephalography (EEG) is one of most commonly used brain signals and EEG recognition is an important part of BCI system. Recently, convolutional neural networks (ConvNet) in deep learning are becoming the new cutting edge tools to tackle the problem of EEG recognition. However, training an effective deep learning model requires a big number of data, which limits the application of EEG datasets with a small number of samples. In order to solve the issue of data insufficiency in deep learning for EEG decoding, we propose a novel data augmentation method that add perturbations to amplitudes of EEG signals after transform them to frequency domain. In experiments, we explore the performance of signal recognition with the state-ofthe-art models before and after data augmentation on BCI Competition IV dataset 2a and our local dataset. The results show that our data augmentation technique can improve the accuracy of EEG recognition effectively.

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“…To decode the target command, EEG-enabled BCI device primarily benefit from state-of-the-art machine learning algorithms. Methods such as deep neural networks [11], [12], generative models [13] and Bayesian models [15] have shown satisfactory performance in these systems.…”

Section: Dorian8x8@berkeleyedumentioning

confidence: 99%

Brain-Computer Interface in Virtual Reality

Abbasi-Asl

Keshavarzi

Chan

2019

2019 9th International IEEE/EMBS Conference on Neural Engineering (NER)

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We study the performance of brain computer interface (BCI) system in a virtual reality (VR) environment and compare it to 2D regular displays. First, we design a headset that consists of three components: a wearable electroencephalography (EEG) device, a VR headset and an interface. Recordings of brain and behavior from human subjects, performing a wide variety of tasks using our device are collected. The tasks consist of object rotation or scaling in VR using either mental commands or facial expression (smile and eyebrow movement). Subjects are asked to repeat similar tasks on regular 2D monitor screens. The performance in 3-D virtual reality environment is considerably higher compared to the to the 2D screen. Particularly, the median number of success rate across trials for VR setting is double of that for the 2D setting (8 successful command in VR setting compared to 4 successful command in 2D screen in 1 minute trials). Our results suggest that the design of future BCI systems can remarkably benefit from the VR setting.

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Generative Adversarial Networks Conditioned by Brain Signals

Cited by 107 publications

References 23 publications

Deep learning-based electroencephalography analysis: a systematic review

Deep learning-based electroencephalography analysis: a systematic review

An Amplitudes-Perturbation Data Augmentation Method in Convolutional Neural Networks for EEG Decoding

Brain-Computer Interface in Virtual Reality

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