## 4 Discussion
[73] presented the accuracies of 73.41% and 73.06% using minimum-Redundancy-Maximum-Relevance (mRMR) for feature selection. [PERSON] et al. [74] performed classification using segment level decision fusion and presented precisions of 76.9% and 69.4% to discriminate LV/HV and LA/HA emotions. In the studies by [PERSON] et al. [48], the discriminative graph regularized extreme learning machine (GELM) with DE features achieved the highest average accuracies of 69.67% for 4-class classification in VA emotion space. The introduced LORSAL classifier presented ideal evaluation metrics for EEG emotion recognition, including the compared NB, SVM, LR_L1, and LR_L2 methods in the experiments. Recently, deep learning (DL) methods have been used for EEG-based emotion classification [28; 29]. In [75], a hybrid DL model combining CNN and RNN learned task-related features from grid-like EEG frames and achieved the accuracies of 72.06% and 74.12 for valence and arousal. The DNN and CNN models by [PERSON] et al. [76] achieved the precisions of 75.78%, 73.12%, 81.41%, and 73.36% along valence and arousal dimensions, respectively. The classification accuracies for valence and arousal were over 85% using LSTM-RNN by [PERSON] et al. [77], and over 87% using 3D-CNN by [PERSON] et al. [78]. More recently, [PERSON] et al. [33; 34] have researched a lot on the combination of DL models and various features. As tabulated in Table 5, computer vision CNN (CVCNN), global spatial filter CNN (GSCNN), and global space local time filter (GSLTCNN) [33] presented obvious improvements with concatenating PSD, raw EEG features, and normalized EEG signals. In [34], the proposed hierarchical bidirectional gated recurrent unit (H-ATT-BGRU) network performed better on raw EEG signals than CNN and LSTM, and the obtained accuracies in valence and arousal dimensions were 67.9% and 66.5% for 2-class cross-subject emotion recognition. For more details about the DL architectures applied in the DEAP data, readers may refer to the literature [33; 34; 75; 76; 77; 78]. Compared to traditional shallow methods, the DL schemes remove the signal pre-processing and feature extraction/selection progress, and are more suitable for affective representation [35; 36]. However, the DL methods cannot reveal the relationship between emotional states and EEG signals for being like a black box [37]. However, more importantly, the training of DL networks is extremely time-consuming, which limits their practical applications in real-time emotion recognition [3]. [PERSON] et al. [28] stated that from practical issues, the DL methods have problems of very long computation, and the vanishing/exploding gradients, and their practical application need extra graphic processing unit (GPU). [PERSON] et al. [29] pointed out that from a practical point-of-view, the hyperparameter search of a DL algorithm often takes up a lot of time for training. Additionally, [PERSON] et al. [28] and [PERSON] et al. [29] make comprehensive reviews on the recent DL schemes. To illustrate the time efficiency, the average training time of the compared NB, SVM, MLR_L1, MLR_L2, and LORSAL methods are shown in Table 6. The average running time for STFT-based feature extraction is 68.15 s. In our experiment, all the programs are performed using on a computer with an Intel Core i5-4590 of 3.30 GHz and 8.00-GB RAM. LORSAL takes just no more than 4 s for training, and the computing time is in the same order as the compared traditional shallow methods. As mentioned earlier, the complexity of LORSAL is \(O((L+1)\,^{2}K)\) for each quadratic problem, where \(L\) is the number of EEG epochs used for training and \(K\) is the number of emotion classes. As shown in Table 5, the time-consumptions of LORSAL on DE, PSD, DASM, RASM, and DCAU (with different dimensions 160, 160, 70, 70, and 55) are nearly the same. Given limited computational resources, or with portable devices, the introduced LORSAL algorithm has higher time efficiency than DL methods and can present better performance than the compared shallow methods.