Electroencephalogram (EEG) is characterized by high temporal resolution, and various EEG analysis methods have developed rapidly in recent years. The EEG microstate analysis method can be used to study the changes of the brain in the millisecond scale, and can also present the distribution of EEG signals in the topological level, thus reflecting the discontinuous and nonlinear characteristics of the whole brain. After more than 30 years of enrichment and improvement, EEG microstate analysis has penetrated into many research fields related to brain science. In this paper, the basic principles of EEG microstate analysis methods are summarized, and the changes of characteristic parameters of microstates, the relationship between microstates and brain functional networks as well as the main advances in the application of microstate feature extraction and classification in brain diseases and brain cognition are systematically described, hoping to provide some references for researchers in this field.
Post-stroke aphasia is associated with a significantly elevated risk of depression, yet the underlying mechanisms remain unclear. This study recorded 64-channel electroencephalogram data and depression scale scores from 12 aphasic patients with depression, 8 aphasic patients without depression, and 12 healthy controls during resting state and an emotional Stroop task. Spectral and microstate analyses were conducted to examine brain activity patterns across conditions. Results showed that depression scores significantly negatively explained the occurrence of microstate class C and positively explained the transition probability from microstate class A to B. Furthermore, aphasic patients with depression exhibited increased alpha-band activation in the frontal region. These findings suggest distinct neural features in aphasic patients with depression and offer new insights into the mechanisms contributing to their heightened vulnerability to depression.
Transcranial alternating current stimulation (tACS) holds significant potential for improving motor function in stroke patients, but its underlying mechanisms remain unclear. In this study, 20 Hz tACS was applied to 15 stroke patients, and their motor imagery (MI) signals were collected before and after stimulation, which were for assessment by combining with the Fugl-Meyer Assessment for Upper Extremity (FMA-UE). Additionally, 11 subjects were recruited as a healthy control group. The study demonstrated that FMA-UE scores of stroke patients significantly increased after tACS intervention. The duration of EEG microstate C and F decreased significantly, while microstate D (coverage, duration, and occurrence probability) increased markedly, and microstate E decreased. The transition probabilities of C→D and D→B were positively correlated with FMA-UE scores. Based on these findings, this study concludes that 20 Hz tACS can enhance neuroplasticity and motor function in patients, and the transition probabilities (C→D/D→B) may serve as potential indicators for assessing motor function, providing experimental evidence for the clinical application of tACS and the development of rehabilitation brain-computer interfaces.