e-ISSN 2251-872X
p-ISSN 2251-8711
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6 resultsUpdated October 20, 2026
1
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2
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3
Identifying Methamphetamine Users Through Electroencephalography Analysis: Harnessing Highly Comparative Time-Series Analysis and Machine Learning Approaches
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4
Challenges and future trends in wearable closed-loop neuromodulation to efficiently treat methamphetamine addiction
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Yun-Hsuan Chen; Jie Yang; Hemmings Wu; Kevin T. Beier; Mohamad Sawan
5
Identification of Methamphetamine Abusers Can Be Supported by EEG-Based Wavelet Transform and BiLSTM Networks
Brain Topography 37(6) (2024)
Hui Zhou; Jiaqi Zhang; Junfeng Gao; Xuanwei Zeng; Xiangde Min; Huimiao Zhan; Hua Zheng; Huaifei Hu; Yong Yang; Shuguang Wei
6
Analysing the behaviour change of brain regions of methamphetamine abusers using electroencephalogram signals: Hope to design a decision support system
Addiction Biology 29(2) (2024)
Sepideh Zolfaghari; Yashar Sarbaz; Ali Reza Shafiee‐Kandjani