Maximum Contrastive Networks for multi-channel SSVEP detection

Embrandiri, Sharat S.; Reddy, M. Ramasubba

doi:10.1109/ner.2015.7146793

Cited by 3 publications

(3 citation statements)

References 9 publications

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“…As shown in Figure 2, the intrusion detection method based on a multichannel autoencoder mainly comprises a multichannel autoencoder and one-dimensional CNN. e model combines the advantages of feature-based detection and anomaly detection methods, using regular traffic and attack traffic to train the autoencoder models of their respective channels, and then utilizes a 1D CNN network to extract the hidden interrelationships in the multichannel description, thereby reducing the imbalance [15]. e influence of data on the model dramatically improves the detection accuracy of the sample, and because the model uses an unsupervised autoencoder and a lightweight 1D CNN network, the detection efficiency of the model is effectively improved.…”

Section: Intrusion Detection Methodmentioning

confidence: 99%

A Metaheuristic Autoencoder Deep Learning Model for Intrusion Detector System

Pandey

Kumar

Lamin

et al. 2022

Mathematical Problems in Engineering

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A multichannel autoencoder deep learning approach is developed to address the present intrusion detection systems’ detection accuracy and false alarm rate. First, two separate autoencoders are trained with average traffic and assault traffic. The original samples and the two additional feature vectors comprise a multichannel feature vector. Next, a one-dimensional convolution neural network (CNN) learns probable relationships across channels to better discriminate between ordinary and attack traffic. Unaided multichannel characteristic learning and supervised cross-channel characteristic dependency are used to develop an effective intrusion detection model. The scope of this research is that the method described in this study may significantly minimize false positives while also improving the detection accuracy of unknown attacks, which is the focus of this paper. This research was done in order to improve intrusion detection prediction performance. The autoencoder can successfully reduce the number of features while also allowing for easy integration with different neural networks; it can reduce the time it takes to train a model while also improving its detection accuracy. An evolutionary algorithm is utilized to discover the ideal topology set of the CNN model to maximize the hyperparameters and improve the network’s capacity to recognize interchannel dependencies. This paper is based on the multichannel autoencoder’s effectiveness; the fourth experiment is a comparative analysis, which proves the benefits of the approach in this article by correlating it to the findings of various different intrusion detection methods. This technique outperforms previous intrusion detection algorithms in several datasets and has superior forecast accuracy.

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Section: Intrusion Detection Methodmentioning

confidence: 99%

A Metaheuristic Autoencoder Deep Learning Model for Intrusion Detector System

Pandey

Kumar

Lamin

et al. 2022

Mathematical Problems in Engineering

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“…Generally, the pre-trained model can be fine-tuned and then employed to a novel related scenario to solve insufficient data and detection accuracy problem [5]. For instance, Embrandiri et al [115] employed denoising autoencoder to pre-train the network and then the network was trained by back-propagation to maximize contrast/SNR, which proves the feasibility of pre-trained model in SSVEP detection. Therefore, the advanced ERPENet in [114] proposed for ERP/P300 classification may provide potential direction for SSVEP-based BCI systems, which can ease the pressure of store and analyze large-scale data.…”

Section: A the Pre-trained Model For Eeg Classificationmentioning

confidence: 99%

Data Analytics in Steady-State Visual Evoked Potential-Based Brain–Computer Interface: A Review

et al. 2021

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Electroencephalograph (EEG) has been widely applied for brain-computer interface (BCI) which enables paralyzed people to directly communicate with and control of external devices, due to its portability, high temporal resolution, ease of use and low cost. Of various EEG paradigms, steady-state visual evoked potential (SSVEP)-based BCI system which uses multiple visual stimuli (such as LEDs or boxes on a computer screen) flickering at different frequencies has been widely explored in the past decades due to its fast communication rate and high signalto-noise ratio. In this paper, we review the current research in SSVEP-based BCI, focusing on the data analytics that enables continuous, accurate detection of SSVEPs and thus high information transfer rate. The main technical challenges, including signal pre-processing, spectrum analysis, signal decomposition, spatial filtering in particular canonical correlation analysis and its variations, and classification techniques are described in this paper. Research challenges and opportunities in spontaneous brain activities, mental fatigue, transfer learning as well as hybrid BCI are also discussed.

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“…There are many methods for extracting features from a single-channel EEG signal of SSVEP, such as epoch-average process [16], fast Fourier transformation (FFT) [17] and wavelet transform (WT) [18]. For multi-channel EEG signals of SSVEP, methods that include Maximum Contrast Combination (MCC) [19] and Canonical Correlation Analysis (CCA) [20], are commonly utilized. The combination of CCA and FFT allows linearly fitting the multi-channel signals and extracting the frequencydomain characteristics.…”

mentioning

confidence: 99%

Multivariate empirical mode decomposition and multiscale entropy analysis of EEG signals from SSVEP-based BCI system

Gao

Zhang

Dang

et al. 2018

EPL

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The steady-state visual evoked potential (SSVEP)-based Brain-Computer Interface (BCI) has been employed in the brain-controlled wheelchair system for patients with severe dyskinesia disease. However, a long-time operation brings about users fatigue, leading to a decrease of performance of the BCI system in practical applications. The characterization of the fatigued mechanism and the improvement of the SSVEP classification accuracy remains a challenging problem of significant importance. In this letter, we first conduct SSVEP experiments to acquire the EEG signals during both normal and fatigued states. Then we develop a novel framework, which integrates the advantages of multivariate empirical mode decomposition (MEMD) and Support Vector Machine (SVM), for improving the SSVEP classification accuracy, especially during the fatigued state. The results suggest that the novel framework enables us to obtain a higher SSVEP classification accuracy compared with the method without MEMD. Furthermore, in order to reveal the fatigued behavior, we use the multivariate multiscale sample entropy (MMSE) to analyze the multi-channel EEG signals corresponding to normal and fatigued states. We interestingly find that the MMSE values in the fatigued state are lower than that in the normal state, which reflects the increase of regularity in SSVEP signals during the fatigued state. That is, a greater synchronization of neural assemblies is required to realize cognitive impairment when fatigue happens. The knowledge for the understanding of the brain fatigued behavior underlying SSVEP-based BCI experiments is gained by our analysis.

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Maximum Contrastive Networks for multi-channel SSVEP detection

Cited by 3 publications

References 9 publications

A Metaheuristic Autoencoder Deep Learning Model for Intrusion Detector System

A Metaheuristic Autoencoder Deep Learning Model for Intrusion Detector System

Data Analytics in Steady-State Visual Evoked Potential-Based Brain–Computer Interface: A Review

Multivariate empirical mode decomposition and multiscale entropy analysis of EEG signals from SSVEP-based BCI system

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