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Performance Enhancement of P300 Detection by Multi-Scale-CNN

Wang, Hongtao and Pei, Zian and Xu, Linfeng and Xu, Tao and Bezerianos, Anastasios and Sun, Yu and Li, Junhua (2021) 'Performance Enhancement of P300 Detection by Multi-Scale-CNN.' IEEE Transactions on Instrumentation and Measurement, 70. pp. 1-12. ISSN 0018-9456

Performance Enhancement of P300 Detection by Multi-Scale-CNN.pdf - Accepted Version

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P300-based spelling system is one of the most popular brain-computer interface applications. Its success largely depends on performance, including the information transmission rate (ITR) and detection rate (i.e., accuracy). To achieve good performance, we proposed a multi-scale convolutional neural network (MS-CNN) model, which consists of seven layers. First, an upfront dataset was used to train the MS-CNN, aiming to obtain a subject-unspecific model (universal model) for P300 detection. Second, this universal model was adapted by a portion of data derived from a subject to update the model to obtain a subject-specific model by incorporating a transfer learning technique. We applied the proposed model in the BCI Controlled Robot Contest at the 2019 World Robot Conference, and our performance was the best among the teams in the contest. In the contest, ten healthy young subjects were randomly assigned by the contest committee to assess the model. Our model achieved the best P300 detection performance (higher accuracy with less repetition time). The ITR for the subject-unspecific case was 13.49 bits/min while the ITR for the subject-specific case was 12.13 bits/min when the repetitions were fewer than six. It is believed that our method may pave a promising path for taking a further step toward efficient implementation of P300-based spelling system.

Item Type: Article
Uncontrolled Keywords: Electroencephalogram (EEG), Subject-Unspecific, Subject-Specific, Multi-Scale Convolutional Neural Network (MS-CNN), Event-Related Potential (ERP)
Divisions: Faculty of Science and Health
Faculty of Science and Health > Computer Science and Electronic Engineering, School of
SWORD Depositor: Elements
Depositing User: Elements
Date Deposited: 25 Mar 2021 16:56
Last Modified: 23 Sep 2022 19:45

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