Temporal Convolutional Learning: A New Sequence-based Structure to Promote the Performance of Convolutional Neural Networks in Recognizing P300 Signals

Q: How can I download an article?

To download an article from SID, first log in to the site, search for the article title, and click on the 'Download Article' option.

Q: How can I download an ISI article?

To download an ISI article on SID, enter the keyword or article title in the search bar, view the relevant results, click on the desired article, and select the 'Download Article' option.

Q: How can I access the SID database?

To access the SID database, visit SID.ir, create an account, and log in to access scientific resources.

Q: Is downloading articles from SID free?

Some articles on SID are available for free, while others require payment. Details are specified on the article's page.

Mardi Mahnaz; Keyvanpour Mohamad Reza; SHOJAEDINI SEYED VAHAB

مرکز اطلاعات علمی Scientific Information Database (SID) - Trusted Source for Research and Academic Resources

Journal Paper

Paper Information

Journal: Health Management and Information Science Year:2021 | Volume:8 | Issue:1 Page(s): 68-77

Download Full-Text

View:

Download:

Cites:

Information Journal Paper

Title

Temporal Convolutional Learning: A New Sequence-based Structure to Promote the Performance of Convolutional Neural Networks in Recognizing P300 Signals

Author(s)

Mardi Mahnaz | Keyvanpour Mohamad Reza | SHOJAEDINI SEYED VAHAB | Issue Writer Certificate

Keywords

EEG signals

P300

Convolutional Neural Networks

Temporal Convolutional Networks

Deep Learning

Brain-Computer Interface

Abstract

Distinguishing P300 signals from other components of the EEG is one of the most challenging issues in Brain Computer Interface (BCI) applications, and machine learning methods have vastly been utilized as effective tools to perform such separation. Although in recent years deep neural networks have significantly improved the quality of the above detection, the significant similarity between P300 and other components of EEG in parallel with their unrepeatable nature have led to P300 detection, which are still an open problem in BCI domain. In this study, a novel architecture is proposed in order to detect P300 signal among EEG, in which the temporal learning concept is engaged as a new substructure inside the main Convolutional Neural Network (CNN). The above Temporal Convolutional Network (TCN) may better address the problem of P300 detection, thanks to its potential in involving time sequence properties in modelling of these signals. The performance of the proposed method is evaluated on the EPFL BCI dataset, and the obtained results are compared in two inter-subject and intra-subject scenarios with the results of classical CNN in which temporal properties of input are not considered. Increased True Positive Rate of the proposed method (an average of 4 percent) and its accuracy (an average of 2. 9 percent) in parallel with the decrease in its False Positive Rate (averagely 3. 1 percent) shows the effectiveness of the TCN structure in promoting the detection procedure of P300 signals in BCI applications.

Multimedia

No record.

Cites

No record.

References

Cite

APA: Copy

Mardi, Mahnaz, Keyvanpour, Mohamad Reza, & SHOJAEDINI, SEYED VAHAB. (2021). Temporal Convolutional Learning: A New Sequence-based Structure to Promote the Performance of Convolutional Neural Networks in Recognizing P300 Signals. HEALTH MANAGEMENT AND INFORMATION SCIENCE, 8(1), 68-77. SID. https://sid.ir/paper/970254/en

Vancouver: Copy

Mardi Mahnaz, Keyvanpour Mohamad Reza, SHOJAEDINI SEYED VAHAB. Temporal Convolutional Learning: A New Sequence-based Structure to Promote the Performance of Convolutional Neural Networks in Recognizing P300 Signals. HEALTH MANAGEMENT AND INFORMATION SCIENCE[Internet]. 2021;8(1):68-77. Available from: https://sid.ir/paper/970254/en

IEEE: Copy

Mahnaz Mardi, Mohamad Reza Keyvanpour, and SEYED VAHAB SHOJAEDINI, “Temporal Convolutional Learning: A New Sequence-based Structure to Promote the Performance of Convolutional Neural Networks in Recognizing P300 Signals,” HEALTH MANAGEMENT AND INFORMATION SCIENCE, vol. 8, no. 1, pp. 68–77, 2021, [Online]. Available: https://sid.ir/paper/970254/en

Related Journal Papers

No record.

Related Seminar Papers

No record.

Related Plans

No record.

Recommended Workshops