Application of Stacked Autoencoders to P300 Experimental Data

Vařeka, Lukáš; Prokop, Tomáš; Mouček, Roman; Mautner, Pavel; Štěbeták, Jan

doi:10.1007/978-3-319-59063-9_17

Cited by 6 publications

(7 citation statements)

References 13 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In our previous work [27], we applied stacked autoencoders (SAE) to the same GTN dataset. In contrast with the current work, manual feature extraction using discrete wavelet transform was performed.…”

Section: Discussionmentioning

confidence: 99%

Evaluation of convolutional neural networks using a large multi-subject P300 dataset

Vařeka

2020

Biomedical Signal Processing and Control

Self Cite

View full text Add to dashboard Cite

Deep neural networks (DNN) have been studied in various machine learning areas. For example, event-related potential (ERP) signal classification is a highly complex task potentially suitable for DNN as signal-to-noise ratio is low, and underlying spatial and temporal patterns display a large intra-and intersubject variability. Convolutional neural networks (CNN) have been compared with baseline traditional models, i.e. linear discriminant analysis (LDA) and support vector machines (SVM) for single trial classification using a large multi-subject publicly available P300 dataset of school-age children (138 males and 112 females). For single trial classification, classification accuracy stayed between 62 % and 64 % for all tested classification models. When applying the trained classification models to averaged trials, accuracy increased to 76 -79 % without significant differences among classification models. CNN did not prove superior to baseline for the tested dataset. Comparison with related literature, limitations and future directions are discussed.

show abstract

Section: Discussionmentioning

confidence: 99%

Evaluation of convolutional neural networks using a large multi-subject P300 dataset

Vařeka

2020

Biomedical Signal Processing and Control

Self Cite

View full text Add to dashboard Cite

show abstract

“…The vast majority of classifier fully-connected layers employed a softmax activation function, whereas non-classifier fully-connected layers used the sigmoid activation function. There were only three SAE studies that discussed activation functions and these did not form a consensus, with [52,53] employing sigmoid activation functions for non-classifier AE layers, while [54] instead using ReLU. More research is needed in order to better understand the most effective activation function for SAE architectures.…”

Section: Activation Functionsmentioning

confidence: 99%

“…Of the ERP task studies, three different types of deep learning architectures were used: SAE's, DBN's, and CNN's. The two SAE studies, [54,74], compared performances between SAE architectures and MLPNN's, both finding SAE's to perform best. Kulasingham et al [75] specifically compared performances between DBN's and SAE's and found that DBN's had a slight advantage in classification accuracy.…”

Section: Erp Tasksmentioning

confidence: 99%

Deep learning for electroencephalogram (EEG) classification tasks: a review

2019

View full text Add to dashboard Cite

Objective. Electroencephalography (EEG) analysis has been an important tool in neuroscience with applications in neuroscience, neural engineering (e.g. Brain-computer interfaces, BCI's), and even commercial applications. Many of the analytical tools used in EEG studies have used machine learning to uncover relevant information for neural classification and neuroimaging. Recently, the availability of large EEG data sets and advances in machine learning have both led to the deployment of deep learning architectures, especially in the analysis of EEG signals and in understanding the information it may contain for brain functionality. The robust automatic classification of these signals is an important step towards making the use of EEG more practical in many applications and less reliant on trained professionals. Towards this goal, a systematic review of the literature on deep learning applications to EEG classification was performed to address the following critical questions: (1) Which EEG classification tasks have been explored with deep learning? (2) What input formulations have been used for training the deep networks? (3) Are there specific deep learning network structures suitable for specific types of tasks? Approach. A systematic literature review of EEG classification using deep learning was performed on Web of Science and PubMed databases, resulting in 90 identified studies. Those studies were analyzed based on type of task, EEG preprocessing methods, input type, and deep learning architecture. Main results. For EEG classification tasks, convolutional neural networks, recurrent neural networks, deep belief networks outperform stacked auto-encoders and multi-layer perceptron neural networks in classification accuracy. The tasks that used deep learning fell into five general groups: emotion recognition, motor imagery, mental workload, seizure detection, event related potential detection, and sleep scoring. For each type of task, we describe the specific input formulation, major characteristics, and end classifier recommendations found through this review.Significance. This review summarizes the current practices and performance outcomes in the use of deep learning for EEG classification. Practical suggestions on the selection of many hyperparameters are provided in the hope that they will promote or guide the deployment of deep learning to EEG datasets in future research.

show abstract

“…In addition to the application of the traditional machine learning method, the deep learning architectures have also been employed in ERP analysis in recent years. For ERP detection tasks, the most popular deep learning architectures are DBN [32], SAE [33], and CNN [34]. Each of the architectures achieved state-of-the-art classification accuracy on EEG classification for ERP detection.…”

Section: Introductionmentioning

confidence: 99%

An EEG Classification-Based Method for Single-Trial N170 Latency Detection and Estimation

Zang

Ding

et al. 2022

Computational and Mathematical Methods in Medicine

View full text Add to dashboard Cite

Event-related potentials (ERPs) can reflect the high-level thinking activities of the brain. In ERP analysis, the superposition and averaging method is often used to estimate ERPs. However, the single-trial ERP estimation can provide researchers with more information on cognitive activities. In recent years, more and more researchers try to find an effective method to extract single-trial ERPs, because most of the existing methods have poor generalization ability or suffer from strong assumptions about the characteristics of ERPs, resulting in unsatisfactory results under the condition of a very low signal-to-noise ratio. In this paper, an EEG classification-based method for single-trial ERP detection and estimation was proposed. This study used a linear generated EEG model containing templates of ERP local descriptors which include amplitude and latency, and this model can avoid the invalid assumption about ERPs taken by other methods. The purpose of this method is not to recover the whole ERP waveform but to model the amplitude and latency of ERP components. This method afterwards examined the three machine learning models including logistic regression, neural network, and support vector machine in the EEG signal classification for ERP detection and selected the best performed MLPNN model for detection. To get the utmost out of information produced in the classification process, this study also used extra information to propose a new optimization model, with which outperformed detection results were obtained. Performance of the proposed method is evaluated on simulated N170 and real P50 data sets, and the results show that the model is more effective than the Woody filter and the SingleTrialEM algorithm. These results are also consistent with the conclusion of sensory gating, which demonstrated good generalization ability.

show abstract

Application of Stacked Autoencoders to P300 Experimental Data

Cited by 6 publications

References 13 publications

Evaluation of convolutional neural networks using a large multi-subject P300 dataset

Evaluation of convolutional neural networks using a large multi-subject P300 dataset

Deep learning for electroencephalogram (EEG) classification tasks: a review

An EEG Classification-Based Method for Single-Trial N170 Latency Detection and Estimation

Contact Info

Product

Resources

About