| 英文摘要 | With the continuous development of the field of brain science and intelligent algorithms, the field of EEG research and the field of machine learning research have more and more intersections. On the one hand, research on the physiological structure and thinking mode of the human brain can promote the development of intelligent algorithms; on the other hand, intelligent algorithms such as machine learning are gradually expanding our understanding of the human brain. In recent years, more and more researchers have begun to use machine learning to analyze EEG signals, such as EEG analysis of patients with brain diseases such as depression and schizophrenia; algorithm research on different brain-computer interfaces such as event-related potentials and motor imagery potentials; brain state analysis such as emotion, attention, etc. However, research on analyzing personality traits based on EEG signals is still in its infancy. Since the factors related to personal traits in EEG data are not as significant as the characteristics of brain diseases and cognitive states, how to use EEG signals to automatically identify the level of subjects' personality traits has always been a challenging problem. Organizational commitment, an important personality trait, has received strong attention in many occupational groups or special populations. Traditional organizational commitment assessment is carried out based on psychological scales. Due to the influence of various social factors, it may be difficult to obtain real results in practical application scenarios. Therefore, in this paper, methods for identifying levels of organizational commitment using brain electrophysiological signals are explored.
For this task, this study is the first to construct a resting-state EEG dataset associated with levels of organizational commitment. In this paper, the resting-state EEG data of subjects was recorded, and the subjects were assigned category labels using organizational commitment scales. A series of different types of features (including features based on power spectrum analysis, micro-state features, functional brain network features and nonlinear dynamic features) in multi-channel resting-state EEG signals were extracted. Then, the binary classification task of organizational commitment levels is realized using different machine learning methods (support vector machines, logistic regression, gradient boosting decision trees, K-nearest neighbor algorithm, Gaussian naive Bayes classifier). In cross-validation experiments, this paper evaluates the performance of different features and different classifiers based on different metrics such as precision, recall, precision, and F1 score. Therefore, the resting-state EEG features that perform relatively well under this task are summarized, namely alpha-band power spectral density, weighted clustering coefficient of brain network, permutation entropy and approximate entropy.
On this basis, this paper further studies the key details of the resting-state EEG classification experiments. Through comparative experiments, it is verified that the EEG in the eyes-open state cannot achieve effective classifications of organizational commitment levels; at the same time, as the time window lengths of EEG samples decrease, the indicators of the classifier show a downward trend; in addition, the study compared the differences in the classification performance of electrodes in different brain regions, and found that when only a small number of electrodes in typical brain regions were used, all indicators of machine learning classification decreased. In order to further improve various indicators such as the overall accuracy of classifications for organizational commitment levels, a multi-layer fusion model framework based on the Stacking strategy was constructed, which achieved an overall classification accuracy of 82.6% and an F1 score of 0.827 for the 4-second resting-state EEG samples; in addition, in order to overcome the classification difficulty when the lengths of the EEG samples are small, this study constructed a series of models for the classification of organizational commitment level based on deep learning methods, including EEG-based one-dimensional convolutional network framework (EEG-1DCNN), EEG-based recurrent neural network framework (EEG-LSTM), two-dimensional convolutional network framework based on EEG spectrogram images (ESI-2DCNN). Furthermore, considering the spatial topological properties of EEG signals, a graph convolutional neural network framework (EEG-GCN) based on EEG-feature topological graphs was constructed. Not only that, this paper makes a detailed comparison of the construction indicators of the topological adjacency matrix of EEG-GCN, and finally realizes the classification of EEG-GCN based on pairwise phase consistency (PPC) and spatial geodetic distance, which achieved an overall accuracy of 79.1% and an F1 score of 0.788 for very short resting-state EEG samples with a duration of 0.5 seconds. This outperforms other forms of deep learning classification frameworks, enabling high-accuracy recognition of shorter EEG samples.
To sum up, this paper conducts a comprehensive exploration of the characteristics of resting-state EEG and the construction of different classification models for the goal of classification of organizational commitment levels. This provides a theoretical and methodological basis for further research on EEG-based organizational commitment analysis or other personality trait analysis. |
修改评论