## 3 Methodology
To leverage the power of deep learning without requiring extensive training data, transfer learning is employed. The pretrained GoogLeNet model, initially trained on large image datasets, is fine-tuned by modifying the last two layers to adapt to the specific task of classifying hand movements from EEG signals. This approach significantly reduces the computational cost and training time while improving the model's ability to generalize across different subjects and tasks. By focusing on the most relevant  
Figure 1: General Framework of the preliminary model and the proposed systems for classification. features learned during pretraining, the GoogLeNet classifier achieves high accuracy in distinguishing between the various motor functions. The integrated use of CSP, CWT, and GoogLeNet forms a robust pipeline for EEG signal classification. CSP enhances discriminative features, CWT captures essential time-frequency information, and GoogLeNet provides powerful classification capabilities. The proposed system demonstrated outstanding performance, achieving high classification accuracies across different motor tasks and subjects, thus highlighting its potential as a general and reliable solution for motor imagery (MI) tasks. Prior to developing the final model, a baseline system was constructed based on the methods described in [17], as the same dataset was used, achieving classification results exceeding 90%. Additionally, a preliminary study model was created to provide a clear overview of the expectations for developing EEG classification models. Although the baseline system employs CSP as mentioned in [17], feature extraction using CSP and CWT to produce scalograms differs from the approach used in the referenced paper. The baseline model differentiates the four hand movements in a pairwise manner, functioning as a binary classifier. However, further improvements were made to the baseline model to modify the binary classification into a four-way classification in the last three models. In the preliminary study, time and frequency features were extracted from the EEG signals and fed into a KNN classifier. The extracted time features included mean, variance, skewness, kurtosis, and area under the signal. The extracted frequency features comprised the power of alpha and beta frequency bands. The last three models (ML Model 1, ML Model 2, and ML-CSP-OVR) were developed based on the baseline model with some modifications. The first two variants, ML Model 1 and ML Model 2, introduce a novel classification technique using multiple stages of SoftMax outputs. The final variant, ML-CSP-OVR, employs a novel combination of CSP OVR (One Versus Rest), CWT, and GoogLeNet for multiclass classification. From the dataset, four segments of data corresponding to the four events/classes of actions (forward hand movement, grasp, release, and reverse) are extracted for classification. These four segments or motor actions are shown in Table 1.  
In the preliminary study, the four events of interest are grouped into two pairs, events 1 and 4 and events 2 and 3, since these pairs involve opposite motor actions. This is grouped under the assumption that the opposite natures involved within these pairs contribute to more accurate classification due to their distinction. For the baseline system, six unique pairs, shown in Table 2, formed from the four classes of motor movement, are used for classification. The rest of the models extend the baseline classification to perform four-way classification without using pairs of two. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Motor Actions Proposed Models** & **Preliminary Study** & **Proposed Models** \\ \hline Forward Hand Movement & Event 1 & FW \\ \hline Grasp & Event 2 & GP \\ \hline Release & Event 3 & RL \\ \hline Reverse Hand Movement & Event 4 & RV \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Descriptions of the labels used for the motor actions in the project.