A new method for P300 detection in deep belief networks: Nesterov momentum and drop based learning rate

Morabbi, Sajedeh; Keyvanpour, Mohammad Reza; Shojaedini, Seyed Vahab

doi:10.1007/s12553-018-0276-9

Cited by 5 publications

(2 citation statements)

References 17 publications

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“…Regarding deep belief networks for EEG classification, in [7] the authors proposed a method to improve the DBN's training algorithm. Testing their models with EEG P300 trials, they were able to achieve a target by block classification accuracy of 93.47% for their subject.…”

Section: Introductionmentioning

confidence: 99%

Single-trial P300 classification using deep belief networks for a BCI system

Cortez

Flores

Andreu-Pérez

2020

2020 IEEE XXVII International Conference on Electronics, Electrical Engineering and Computing (INTERCON)

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A brain-computer interface (BCI) aims to provide their users the capability to interact with machines only through their though processes. BCIs targeted at subjects with mild and severe motor impairments are of special interest since this kind of technology would improve their lifestyles. This paper focuses on the classification of the P300 waveform from single trials in EEG to be used in a BCI using deep belief networks. This deep learning algorithm has the capability to identify relevant features automatically from the subject's EEG data, making its training requiring less preprocessing stages. The network is tested on healthy subjects and post-stroke victims. The highest accuracy achieved was of 91.6% for a healthy subject and 88.1% for a post-stroke victim.

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Section: Introductionmentioning

confidence: 99%

Single-trial P300 classification using deep belief networks for a BCI system

Cortez

Flores

Andreu-Pérez

2020

2020 IEEE XXVII International Conference on Electronics, Electrical Engineering and Computing (INTERCON)

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“…Lots of approaches were proposed for P300 detection [ 6 , 7 , 8 , 9 ]. Recently, the machine learning based methods achieved excellent performance on P300 detection [ 10 , 11 , 12 ]. For the traditional machine learning methods, feature extraction and classification are two of the key techniques.…”

Section: Introductionmentioning

confidence: 99%

Novel Convolutional Neural Network with Variational Information Bottleneck for P300 Detection

Liao

Xu²,

2020

Entropy

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In the area of brain-computer interfaces (BCI), the detection of P300 is a very important technique and has a lot of applications. Although this problem has been studied for decades, it is still a tough problem in electroencephalography (EEG) signal processing owing to its high dimension features and low signal-to-noise ratio (SNR). Recently, neural networks, like conventional neural networks (CNN), has shown excellent performance on many applications. However, standard convolutional neural networks suffer from performance degradation on dealing with noisy data or data with too many redundant information. In this paper, we proposed a novel convolutional neural network with variational information bottleneck for P300 detection. Wiht the CNN architecture and information bottleneck, the proposed network termed P300-VIB-Net could remove the redundant information in data effectively. The experimental results on BCI competition data sets show that P300-VIB-Net achieves cutting-edge character recognition performance. Furthermore, the proposed model is capable of restricting the flow of irrelevant information adaptively in the network from perspective of information theory. The experimental results show that P300-VIB-Net is a promising tool for P300 detection.

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EEG Classification Based On Deep Learning

Wang

Nan²,

2022

2022 IEEE 10th Joint International Information Technology and Artificial Intelligence Conference (ITAIC)

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A new method for P300 detection in deep belief networks: Nesterov momentum and drop based learning rate

Cited by 5 publications

References 17 publications

Single-trial P300 classification using deep belief networks for a BCI system

Single-trial P300 classification using deep belief networks for a BCI system

Novel Convolutional Neural Network with Variational Information Bottleneck for P300 Detection

EEG Classification Based On Deep Learning

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