# Deep Learning-Based Classification of Fine Hand Movements from Low Frequency EEG
## 1 Introduction  
Several BCI systems for motor rehabilitation or motor control [1, 2, 3, 4, 5, 6] and other basic neuroscience studies strongly rely on the ability to precisely and effectively distinguish different fine hand movements. One example is the investigation of the neural mechanisms underlying the writing and the music performance [7, 8] or during real-life performance in ecologically valid situations outside the laboratory [9]. Movement-related cortical potentials (MRCPs) are amplitude modulations of the time-domain EEG signal that occur in the \((0.3,3)\) Hz frequency band [10]. MRCPs can be detected during motor execution, motor attempt, and even during imagery of a movement, and they reflect the cortical processes involved in the planning and execution of a movement [11]. Previous literature [10] reports that the components of the MRCPs can be influenced by several factors, such as the preparatory state (self-paced or cue-based), the level of intention [12], the type of movement, the praxis, and the previous experience of the same movement. Nevertheless, it has been found that MRCPs can also encode several properties of the movements, such as the type of grasp [13], the force level [14], and the speed of the task [15]. In addition, MRCPs have been previously employed to discriminate hand movements in patients with severe manual impairments [5]. For this reason, MRCPs are considered valid signals tobe used for BCI control [5; 6]. Based on this well-established neuroscience background, our aim was to test if a deep learning (DL) approach can improve the performance of the classification of touch, grasp, palmar, and lateral grasp movements. Previous literature has already investigated the classification of different fine hand movements, including touch and different kinds of grasp [16; 17; 18]. Shrinkage linear discriminant analysis (sLDA) and random forest (RF) are well-established approaches for electroencephalography (EEG) classification, showing low complexity and good performance even with a limited amount of trials. However, they might score poorly in case of complex nonlinear EEG data [19]. However, deep DL has recently demonstrated promising results in decoding brain activity in several scenarios, e.g., sleep stage scoring [20], epileptic seizure detection [21], as well as hand movement classification [22]. Therefore, the aim of this work was to evaluate the performance of a newly proposed DL-based model, compared to the well-established sLDA and RF methods, in the classification of three different classes of movement, using two pre-recorded datasets. Given the small size of our datasets, we decided to adopt a CNN-based model to classify MRCPs, as it has been previously showed to be effective in small datasets [23; 24; 25].  
The paper is organized as follows: in Section 2, we present the most relevant previous studies related to our work; in Section 3, we describe the experimental protocol, the common steps of pre-processing for all models, our proposed CNN model, and we briefly review the two baseline models chosen for comparison. In Section 4, we report and discuss all results, both from the qualitative analysis of the MRCPs and of the classification from different movements. Finally, Section 5 concludes the paper and mentions the possible impact of this work for other studies.