Spatial and Time Domain Feature of ERP Speller System Extracted via Convolutional Neural Network

Yoon, Jaehong; Lee, Jung-Nyun; Whang, Mincheol

doi:10.1155/2018/6058065

Cited by 8 publications

(5 citation statements)

References 35 publications

(29 reference statements)

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“…Occlusion sensitivity techniques [92,26,175] use a similar idea, by which the decisions of the network when different parts of the input are occluded are analyzed. [135,211,86,34,87,200,182,122,170,228,164,109,204,85,25] Analysis of activations [212,194,87,83,208,167,154,109] Input-perturbation network-prediction correlation maps [149,191,67,16,150] Generating input to maximize activation [188,144,160,15] Occlusion of input [92,26,175] Several studies used backpropagation-based techniques to generate input maps that maximize activations of specific units [188,144,160,15]. These maps can then be used to infer the role of specific neurons, or the kind of input they are sensitive to.…”

Section: Inspection Of Trained Modelsmentioning

confidence: 99%

“…Reactive ERP [17,31,211,230] Heard speech decoding [125] RSVP [31, 71, [135] [192] [4, 85,128,190] Event annotation [224] Prediction [141,200] [199]…”

Section: Subjectsmentioning

confidence: 99%

“…The most frequent model inspection techniques involved the analysis of the trained model's weights [41,96,97,120,133,147,182,189,201,220,223,230,246]. This often requires focusing on the weights of the first layer only, as their interpretation in regard to the input data is straightforward.…”

Section: Inspection Of Trained Modelsmentioning

confidence: 99%

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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: Inspection Of Trained Modelsmentioning

confidence: 99%

“…Reactive ERP [17,31,211,230] Heard speech decoding [125] RSVP [31, 71, [135] [192] [4, 85,128,190] Event annotation [224] Prediction [141,200] [199]…”

Section: Subjectsmentioning

confidence: 99%

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Deep learning-based electroencephalography analysis: a systematic review

et al. 2019

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“…In future studies, more categories of visual objects will be used to support and to expand the neural mechanisms of perceptual closure found in this study. In related works, P300 ERP component was used for brain computer interface study [ 25 – 27 ], the ERP components in this study also can be used to build an affective brain computer interface. BCI also can be very useful for the elder people [ 28 – 31 ].…”

Section: Discussionmentioning

confidence: 99%

The Time Course of Perceptual Closure of Incomplete Visual Objects: An Event-Related Potential Study

Liu

Sha

Zhang

et al. 2020

Computational Intelligence and Neuroscience

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Perceptual organization is an important part of visual and auditory information processing. In the case of visual occlusion, whether the loss of information in images could be recovered and thus perceptually closed affects object recognition. In particular, many elderly subjects have defects in object recognition ability, which may be closely related to the abnormalities of perceptual functions. This phenomenon even can be observed in the early stage of dementia. Therefore, studying the neural mechanism of perceptual closure and its relationship with sensory and cognitive processing is important for understanding how the human brain recognizes objects, inspiring the development of neuromorphic intelligent algorithms of object recognition. In this study, a new experiment was designed to explore the realistic process of perceptual closure under occlusion and intact conditions of faces and building. The analysis of the differences in ERP components P1, N1, and Ncl indicated that the subjective awareness of perceptual closure mainly occurs in Ncl, but incomplete information has been processed and showed different manners compared to complete stimuli in N170 for facial materials. Although occluded, faces, but not buildings, still maintain the specificity of perceptual processing. The Ncl by faces and buildings did not show significant differences in both amplitude and latency, suggesting a “completing” process regardless of categorical features.

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“…e proposed model o ered an AUC of 86.1%. Yoon et al [238] provided a way to analyze the spatial and temporal features of ERP. e authors trained a CNN with two convolutional layers whose feature maps represented spatial and temporal features of the event-related potential.…”

Section: Evoked Potentialmentioning

confidence: 99%

A Survey on Deep Learning-based Non-Invasive Brain Signals:Recent Advances and New Frontiers

Zhang

Yao

Wang

et al. 2019

Preprint

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Brain-Computer Interface (BCI) bridges human's neural world and the outer physical world by decoding individuals' brain signals into commands recognizable by computer devices. Deep learning has enhanced the performance of brain-computer interface systems signi cantly in recent years. In this article, we systematically investigate brain signal types for BCI and related deep learning concepts for brain signal analysis. We then present a comprehensive survey of deep learning techniques used for BCI, by summarizing over 230 contributions, most published in the past ve years. Finally, we discuss the applied areas, emerging challenges, and future directions for deep learning-based BCI.

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Spatial and Time Domain Feature of ERP Speller System Extracted via Convolutional Neural Network

Cited by 8 publications

References 35 publications

Deep learning-based electroencephalography analysis: a systematic review

Deep learning-based electroencephalography analysis: a systematic review

The Time Course of Perceptual Closure of Incomplete Visual Objects: An Event-Related Potential Study

A Survey on Deep Learning-based Non-Invasive Brain Signals:Recent Advances and New Frontiers

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