Repetitive TMS-based Identification of Methamphetamine-Dependent Individuals Using EEG Spectra
Fuente:
arXiv
Gespeichert in:
| Hauptverfasser: | , , , , , , , , , , |
|---|---|
| Format: | Preprint |
| Veröffentlicht: |
2025
|
| Schlagworte: | |
| Online-Zugang: | |
| Tags: |
Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
|
| _version_ | 1866918148584767488 |
|---|---|
| author | Zeng, Ziyi Chen, Yun-Hsuan Gao, Xurong Zheng, Wenyao Wu, Hemmings Zhu, Zhoule Yang, Jie Wang, Chengkai Zhong, Lihua Cheng, Weiwei Sawan, Mohamad |
| author_facet | Zeng, Ziyi Chen, Yun-Hsuan Gao, Xurong Zheng, Wenyao Wu, Hemmings Zhu, Zhoule Yang, Jie Wang, Chengkai Zhong, Lihua Cheng, Weiwei Sawan, Mohamad |
| contents | The impact of repetitive transcranial magnetic stimulation (rTMS) on methamphetamine (METH) users' craving levels is often assessed using questionnaires. This study explores the feasibility of using neural signals to obtain more objective results. EEG signals recorded from 20 METH-addicted participants Before and After rTMS (MBT and MAT) and from 20 healthy participants (HC) are analyzed. In each EEG paradigm, participants are shown 15 METH-related and 15 neutral pictures randomly, and the relative band power (RBP) of each EEG sub-band frequency is derived. The average RBP across all 31 channels, as well as individual brain regions, is analyzed. Statistically, MAT's alpha, beta, and gamma RBPs are more like those of HC compared to MBT, as indicated by the power topographies. Utilizing a random forest (RF), the gamma RBP is identified as the optimal frequency band for distinguishing between MBT and HC with a 90% accuracy. The performance of classifying MAT versus HC is lower than that of MBT versus HC, suggesting that the efficacy of rTMS can be validated using RF with gamma RBP. Furthermore, the gamma RBP recorded by the TP10 and CP2 channels dominates the classification task of MBT versus HC when receiving METH-related image cues. The gamma RBP during exposure to METH-related cues can serve as a biomarker for distinguishing between MBT and HC and for evaluating the effectiveness of rTMS. Therefore, real-time monitoring of gamma RBP variations holds promise as a parameter for implementing a customized closed-loop neuromodulation system for treating METH addiction. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2508_11312 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Repetitive TMS-based Identification of Methamphetamine-Dependent Individuals Using EEG Spectra Zeng, Ziyi Chen, Yun-Hsuan Gao, Xurong Zheng, Wenyao Wu, Hemmings Zhu, Zhoule Yang, Jie Wang, Chengkai Zhong, Lihua Cheng, Weiwei Sawan, Mohamad Neurons and Cognition Machine Learning Signal Processing The impact of repetitive transcranial magnetic stimulation (rTMS) on methamphetamine (METH) users' craving levels is often assessed using questionnaires. This study explores the feasibility of using neural signals to obtain more objective results. EEG signals recorded from 20 METH-addicted participants Before and After rTMS (MBT and MAT) and from 20 healthy participants (HC) are analyzed. In each EEG paradigm, participants are shown 15 METH-related and 15 neutral pictures randomly, and the relative band power (RBP) of each EEG sub-band frequency is derived. The average RBP across all 31 channels, as well as individual brain regions, is analyzed. Statistically, MAT's alpha, beta, and gamma RBPs are more like those of HC compared to MBT, as indicated by the power topographies. Utilizing a random forest (RF), the gamma RBP is identified as the optimal frequency band for distinguishing between MBT and HC with a 90% accuracy. The performance of classifying MAT versus HC is lower than that of MBT versus HC, suggesting that the efficacy of rTMS can be validated using RF with gamma RBP. Furthermore, the gamma RBP recorded by the TP10 and CP2 channels dominates the classification task of MBT versus HC when receiving METH-related image cues. The gamma RBP during exposure to METH-related cues can serve as a biomarker for distinguishing between MBT and HC and for evaluating the effectiveness of rTMS. Therefore, real-time monitoring of gamma RBP variations holds promise as a parameter for implementing a customized closed-loop neuromodulation system for treating METH addiction. |
| title | Repetitive TMS-based Identification of Methamphetamine-Dependent Individuals Using EEG Spectra |
| topic | Neurons and Cognition Machine Learning Signal Processing |
| url | https://arxiv.org/abs/2508.11312 |