关键词: 神经网络, 模型解释, 抑郁症, 脑电分类, 云计算

Abstract:

Online EEG classification can accurately assess the brain status of patients with Major Depression Disable（MDD） and track their development status in time, which can minimize the risk of falling into danger and suicide. However, it remains a grand research due to the embedded intensive noises and the intrinsic non-stationarity determined by the evolution of brain states, the lack of effective decoupling of the complex relationship between brain region and neural network during the attack of brain diseases. This study designs an online EEG classification system aided by cloud centering on a CNN. Experiments on depression evaluation has been performed against raw EEG without the need for preprocessing and feature extraction to distinguish Healthy & MDD. Results indicate that MDD can be identified with an accuracy, sensitivity, and specificity of 99.08%, 98.77% and 99.42%, respectively. Furthermore, the experiments on quantitative interpretation of CNN illustrate that there are significant differences between the left and right temporal lobes of depression patients and normal control group.

Key words: neural network, model interpretation, depression, EEG classification, cloud computation