Objective assessment and treatment of depression based on multimodal affective brain computer interfaces
10.3760/cma.j.cn113661-20201224-00018
- VernacularTitle:基于多模态情感脑机接口的抑郁症客观评估与调控治疗
- Author:
Bao-Liang LU
1
;
Yaqian ZHANG
;
Wei LIU
;
Wei-Long ZHENG
Author Information
1. 上海交通大学计算机科学与工程系仿脑计算与机器智能研究中心 200240
- Publication Type:Journal Article
- Keywords:
Depressive disorder;
Multimodal affective brain-computer interface
- From:
Chinese Journal of Psychiatry
2021;54(4):243-251
- CountryChina
- Language:Chinese
-
Abstract:
This paper explores the application of multimodal affective brain-computer interfaces(aBCI) in the diagnosis based on the objective assessment of depression and treatment of deep brain stimulation for refractory depression. In the objective assessment of depression, the traditional depression scales are transformed into the interactive affective tasks. Based on the multimodal aBCI systems, multimodal physiological data including EEG, eye movement, etc, are collected simultaneously. Through deep learning, such as multimodal fusion and transfer learning, an objective assessment systems that can acurately distinguish depression states is established. In the treatment of refractory depression with deep brain stimulation, based on multimodal aBCI and reinforcement learning algorithms, the autonomously adjustment and personalization of the parameters are possible to improve the treatment outcomes.
