Classification of Motor Imagery EEG Signals Based on Channel Attention Mechanism

Yu, Yue; Ji, Wang; Zhao, Liqun; Sun, Zhongbo; Liu, Keping

doi:10.1109/ddcls58216.2023.10167410

Cited by 1 publication

(1 citation statement)

References 29 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…So, this paper uses a 0.016∼ 70Hz IIR Butterworth filter for frequency domain filtering [20] to eliminate low-frequency interference such as working frequency noise, reduce baseline drift, and inhibit highfrequency interference. Additionally, in order to mitigate the impact of artifacts on signal amplitudes, the common average reference montage was selected [21]. Then the signal is recalculated under this reference montage.…”

Section: Pre-processingmentioning

confidence: 99%

Automatic BECT Spike Detection Algorithm Based on Adaptive Template Matching and Convolution Neural Network

Wu,

Xiong,

et al. 2024

Preprint

View full text Add to dashboard Cite

Spike is an important and widely accepted biomarker used in the diagnosis of epilepsy based on scalp electroencephalogram (sEEG). Due to the similarity of its waveform with non-epileptic brain activity and artifacts, it is essential to develop an automated spike detection method based on waveform characteristics to assist physicians. Therefore, this paper proposes a novel algorithm for automatic spike detection that uses an adaptive template matching method as a pre-processor to optimise the selection of candidate spikes for efficient segmentation of sEEG signals. The process aims to eliminate the impact of data overlap caused by sliding window segmentation. The 1D convolutional neural network with a simple structure achieves excellent performance. Besides, it requires fewer trainable parameters and imposes a smaller demand on computational resources. The proposed method is evaluated on sEEG data recorded at Zhejiang University Children's Hospital. Moreover, the experimental results demonstrate that our model achieves average accuracy, sensitivity, specificity, F1 score, and AUC of 98.7\%, 97.39\%, 100\%, 98.67\%, and 99.3\%, respectively.

show abstract

Section: Pre-processingmentioning

confidence: 99%