# Deep Learning-Based Classification of Fine Hand Movements from Low Frequency EEG
## 4 Results and Discussion
### Classification Results  
Tables 1 and 2 report the comparison of the classification performance between the CNN and the baseline models over the unseen validation sets. They show the results of the classification in terms of accuracy. To achieve these performances, we used the CNN model with the best selection of hyperparameters, employing the same architecture for all participants. On the other hand, for sLDA and RF, we considered all possible choices of the sliding window length, with the best window time location, for each participant. The chance level was computed as in Section 3.5, and it was found to be 0.40. Comparing the classification results among the three classifiers, similar accuracies were observed for all of them, with all values above the chance level. Furthermore, they achieved slightly better results in the _Dataset 1_, as expected from its higher repeatability across EEG segments (see Figure 3), compared to the _Dataset 2_. However, for both datasets, the CNN model reached the best average accuracy across all participants (0.70 for _Dataset 1_, 0.64 for _Dataset 2_). Previous results on _Dataset 2_ were obtained using sLDA for the binary classification of right hand palmar vs. lateral movements [17]. There, a grand-average participant-specific peak accuracy of \(66,3\%\) was reached (individual results were not available), in line with our classification outcomes. sLDA and RF achieved the best classification accuracy at the single-subject level in _Dataset 1_: thus, a particular configuration (i.e., an optimal choice of the window length and time location) can lead a baseline model to yield higher performance compared to CNN. Nevertheless, especially for the _Dataset 2_, the CNN showed higher variability in the individual participant accuracies, with some of them reaching very high values (0.80 for G12) and others slightly above the chance level (0.43 for G02). Finally, from the confusion matrices (not reported here for space constraints), we observed that the rest class was classified with the highest accuracy compared to the other movement classes (best accuracy among the two datasets: 78% for rest, 57% for touch, 62% for grasp, 55% for palmar, 52% for lateral), in line with previous literature [16; 17; 18]. Precision and recall metrics have also been computed for all subjects, in both dataset (results are reported in Tables A1-A4 in the Appendix A). We obtained an average value of \(0.68\pm 0.12\) both for precision and recall in _Dataset 1_ (average across subjects and models); similarly, we obtained an average value of \(0.60\pm 0.08\) for precision and \(0.61\pm 0.08\) for recall in _Dataset 2_. As expected, a critical drawback of the CNN approach is its computational complexity, significantly higher with respect to sLDA and RF: the former use less time points as an input to train the model (either 0.6 s, 0.8 s and 1 s), while the latter took the entire 5 s EEG segment into account. Therefore, the training time for a single model is significantly lower compared to CNN: for a single subject, a single run of classification takes 0.46 s for sLDA, 0.56 s for RF, and 29.16 s for CNN. However, we should notice that sLDA and RF require a significant pre-processing step to select the optimal location of the time window in the EEG segment for the classification. In addition, a 10 times 5-fold CV is applied. Then, overall, the time required to classify one single subject by sLDA increases to 3 min, to 10 min for RF, while the CNN requires 31 min. In addition, CNN implements a more complex architecture compared to sLDA and RF. However, it showed promising advantages over sLDA and RF: indeed, the latter exploit a semi-quantitative pre-processing pipeline, including ICA to clean data from eye movements artifacts. Moreover, for both sLDA and RF, we needed to train a classifier at each time point to select the one that led to the best performance.