# Deep Learning-Based Classification of Fine Hand Movements from Low Frequency EEG
## 2 Related Works  
The possibility of decoding touch and grasp actions from low-frequency EEG signals has been shown in other studies [16; 17; 18]. Well-established literature proved that brain activity in the frequency band 2-5 Hz contains relevant movement-related information. Thus, in [26], the authors proposed an interesting approach to classify speed in hand movements: they applied wavelet-CSP to MRCPs and were able to classify slow and fast movements with 83.71% accuracy. In [18], [PERSON] et al. classified single upper limb movements with a binary classification approach, recording six different types of movements, both executed and imagined, and rest trials. For the executed movements, in the _movements versus rest_ binary classification, the average accuracy reached the value of 87%, while, for the _movements versus movements_, the average accuracy dropped down to 55%. For the imagined movements, an accuracy of as less as 27% and 73% was obtained for _movements versus movements_ and _rest versus movements_ classification, respectively. In [16], palmar, lateral, and pincer grasps were recorded and classified, in a cue-based paradigm. A 4-class sLDA was used to classify the three movements and the rest data, obtaining a peak accuracy of 65.9%. Moreover, a binary classifier was trained in the same study, for each binary combination of classes. The palmar versus lateral grasp classification obtained a peak accuracy of 73.5%. In [17], MRCP was shown to significantly discriminate between unimanual (e.g., left hand vs. right hand grasps) as well unimanual vs. bimanual movements. Particularly, both unimanual and bimanual reach and grasp actions were classified with sLDA based on low-frequency time-domain EEG in the frequency range 0.3-3 Hz. Binary combinations of the different movements were also classified separately, leading to average accuracies for the movement classes between 66% and 70%. The highest accuracies were obtained with the _rest_ class versus the _movement_ ones, with performance between 74% to 90%. Recently, new approaches have been increasing. DL showed promising results in many different fields of application and has been successfully applied also in the BCI field [27]. It provided satisfactory outcomes in a variety of EEG analysis, ranging from channel selection to classification of motor imagery (MI). Among other models, CNN was particularly successful to extract spatial and frequency features from EEG for speech classification, as reported in [28], to detect artifacts in EEG [21], as well as to recognize MI for BCI [25]. In the works by [PERSON] [23] and [PERSON] [24], the possibility of MI-EEG classification using a CNN architecture was explored. In both studies, the input of the CNN was represented by the raw EEG signal, without any pre-processing. In [23], fist movements were classified and transfer learning was also used, reaching a subject-specific accuracy of 86.49%, and a standard accuracy of 80.38%. In [24], instead, four MI classes were analyzed, namely elbow extension, twisting, grasping, and rest. The average accuracy obtained was 84%. It is worth noting that these results were obtained with a relatively low amount of data: only 50 trials for each class were available, for a total of 150 trials for the training of the DL architecture.