## 3 Methodology  
The proposed models in this paper (Figure 1) utilize a combination of CSP filtering, CWT, and GoogLeNet classification. We employed the Common Spatial Patterns (CSP) filter, Continuous Wavelet Transform (CWT), and GoogLeNet classifier for their respective strengths in handling EEG signal classification. CSP is particularly effective for binary classification, enhancing discriminative power by maximizing variance differences between classes. CWT was chosen for its ability to analyze non-stationary signals, capturing both temporal and frequency information crucial for understanding EEG dynamics during motor tasks. By transforming signals into scalograms, CWT preserves important features across different time scales for deep learning models. GoogLeNet, a deep convolutional neural network, was utilized for its robust feature learning and classification capabilities. Leveraging transfer learning, GoogLeNet enhances performance even with smaller datasets by using pretrained weights from large datasets, improving generalization across subjects and tasks. This combination addresses EEG classification challenges, such as non-stationarity and subject variability, demonstrating high accuracy in binary and multiclass tasks, making it a robust choice for developing reliable BCI systems for motor imagery and hand movement detection. I have provided an expanded introduction of the applied methods with more detailed explanations for CSP filtering, Continuous Wavelet Transform (CWT), and the GoogLeNet classifier, as shown below. **Common Spatial Pattern (CSP) Filtering**  
Common Spatial Pattern (CSP) is a feature extraction technique widely used in the analysis of EEG signals, particularly for classifying motor imagery tasks. CSP works by finding spatial filters that maximize the variance of the signal for one class while minimizing it for the other. This makes it highly effective for distinguishing between different mental states or movements. The CSP algorithm begins by computing the covariance matrices of the EEG signals for each class. These matrices are then averaged and decomposed using eigenvalue decomposition to find spatial filters that create new signals with optimal variance separation between the classes. The filtered signals highlight the most discriminative features for classification. In this research, CSP filters were applied to the EEG signals to enhance the differences between various hand movements (e.g., forward movement vs. grasp). The filtered signals serve as the basis for subsequent feature extraction and classification processes. **Continuous Wavelet Transform (CWT)**  
Continuous Wavelet Transform (CWT) is a powerful tool for analyzing non-stationary signals such as EEG. Unlike Fourier transform, which only provides frequency information, CWT offers both time and frequency localization, making it ideal for capturing transient features in EEG data. CWT transforms the EEG signals into a time-frequency domain using wavelets, which are functions that can be scaled and translated. The analytic Morse wavelet, known for its good time-frequency localization properties, was chosen for this transformation. The result is a set of wavelet coefficients that represent the signal's frequency content at different times. These coefficients are then used to generate scalograms--visual representations of the time-frequency characteristics of the EEG signals. Scalograms effectively capture the temporal dynamics and frequency information of the EEG, which are crucial for distinguishing between different motor tasks. **GoogLeNet Classifier**  
GoogLeNet, also known as Inception-v1, is a deep convolutional neural network that has shown exceptional performance in various image classification tasks. Its architecture consists of multiple convolutional layers that learn hierarchical features from the input data. In this research, the scalograms generated from the CWT are used as input images for the GoogLeNet classifier.