## 2 Literature Review  
During the research for this literature review, it was observed that most studies focus on motor imagery (MI) data rather than actual movement data. However, the techniques used with MI can be applicable to actual movement EEG data since MI activates similar brain areas and preserves the same temporal characteristics [6]. Therefore, this section discusses papers using both MI and actual movement data. Decades of research have gone into developing EEG-based BCI systems since Dr. [PERSON] first recorded EEG signals from humans in the 1930s [7]. Despite this extensive research, a universally reliable system has yet to be developed. The primary challenge is the highly non-stationary nature of brain signals, which are prone to contamination by artifacts such as eye movements, heartbeat signals (ECG), and electrical noise. Additionally, these signals are subject-dependent and vary across different trials, making it difficult to classify even between two movement activities from EEG signals. The difficulty increases with multiclass systems [8]. To overcome these challenges, several techniques have been developed. One popular and efficient technique is Common Spatial Patterns (CSP), which was initially developed to distinguish between two classes [9; 10]. The CSP algorithm finds spatial filters that maximize the variance of the filtered signal under one condition while minimizing it under the other condition [11]. Paired with an 8-30 Hz broadband filter, CSP has achieved accuracies of 84-94% in discriminating three actions, left-hand movement, right-hand movement, and right-foot movement, within three subjects. An extension of CSP, using pairwise classification and majority voting, has also been described to enable multiclass classification [9]. Other extensions include CSP One-Vs-Rest (OVR), which computes spatial patterns for each class against all others [12; 13], and CSP Divide-And-Conquer (DC), which adopts a tree-based classifier approach [13]. Since the effectiveness of CSP generally relies on optimal participant-specific frequency, it is nearly impossible to obtain spatial filters that generalize for optimal discrimination of classes for every person. Several approaches have been employed to address this issue [13; 14]. Filter Bank Common Spatial Patterns (FBCSP), which autonomously selects the subject-specific frequency range for band-pass filtering of EEG signals [13]; non-conventional FBCSP, which differentiates classes using a fixed set of four frequency bands, reducing the computational cost [8]; and sliding window discriminative CSP (SWDCSP), which uses a sliding window of overlapping frequencies to filter the EEG signals [15]. This paper proposes a model using CSP OVR and CNN with the WAY-EEG-GAL dataset to distinguish four hand movements. Some closely related papers are explored. In [16], the BCI competition III dataset 3a was used, where each subject performed four different motor imagery tasks (left hand, right hand, foot, and tongue). Forty-three channels were used out of sixty-four available. CSP was applied with five filters ranging from 8 Hz to 28 Hz, with increments of 4 Hz. A convolutional neural network (CNN) was used as a classifier, with 80% of the data for training and 20% for validation, resulting in a validation accuracy of 93.75% for intersubject classification using combined data. The author of [17] used the WAY-EEG-GAL dataset, where twelve participants performed continuous movements of a single limb to grasp and lift an object. From these continuous movements, six segments of interest (hand start movement, grasp, lift, hold, replace, and release) were selected for classification. CSP performed six binary classifications on the pairs formed from the six segments. Continuous wavelets transform (CWT) generated scalograms, which were inputs for the CNN network. The classification used was intersubject classification without leave-one-out. The highest accuracy was for the 'hold' segment at 96.1%, and the lowest was for'replace' at 92.9%.