## 1 Introduction
Fine-tuning can be seen as a form of domain adaptation because it allows the model to adapt to the new dataset by adjusting its weights and biases. Fine tuning in convolutional neural networks (CNN) refers to the process of taking a pre-trained CNN and adjusting its parameters to fit a new dataset or task. This is completed by freezing the early layers of the network, which have learned general features,and training only the subsequent layers on the new data. In ref. [19] a transfer CNN framework based on VGG-16 is proposed. A pre-trained VGG-16 CNN model is transferred to a target CNN model, and front-layer parameters are frozen while later-layer parameters are fine-tuned with a target MI dataset composed of time-frequency spectrum images of EEG signals. The proposed framework outperforms traditional methods on the BCI competition IV dataset. In [20], the authors pre-trained a multi-scale convolutional neural network (MSCNN) on a large-scale EEG dataset and fine-tuned it on a target dataset. They show the effectiveness of the proposed MSCNN model on two different EEG P300 datasets. Fine tuning can be a powerful technique for transfer learning, where knowledge gained from the source domain can be applied to the target domain. Additionally, it is useful when we have limited labeled data but want to leverage pre-trained models for our task [2,21]. However, fine-tuning a pre-trained model can result in the loss of specialized knowledge when the target domain has different data characteristics than the source domain, potentially resulting in suboptimal performance [22]. Therefore, subspace learning based-TL is coming to overcome the fine-tune-based TL limitations. Subspace alignment is a technique that involves mapping data from one domain to another by finding a common subspace between the two domains. By aligning the subspaces, we can transfer knowledge from one domain to another and improve the performance of our model. One advantage of subspace alignment is that it is a simple and effective method. In ref. [23], the authors focused on the issues of cross-session and cross-subject classification, which refer to the use of data from past sessions or users to initialize the classifier for a calibration-less BCI mode. The study employs spatial covariance matrices of EEG signals, which use Riemannian geometry to represent data on the manifold of Symmetric Positive Definite (SPD) matrices. The study proposes an affine transformation of the covariance matrices for every session/subject to center them with respect to a reference covariance matrix, making them comparable. But Riemannian alignment (RA) has some limitations in that the classifier must be Riemannian only, it needs labels for ERP datasets, and it is also time-consuming. To tackle these limitations, [PERSON] and [PERSON] [24] proposed an unsupervised and fast approach that is useful in both domains Euclidean and Romanian. It is called the Euclidean alignment (EA) technique, which aligns the feature spaces of the source and target domains to Euclidean space to reduce the distribution shift between the two domains. In the current study, we propose a new approach based on transfer learning. Due to the subject dependency problem of P300-based ERP signals, it is a significant challenge for real-time P300-based brain-computer interface applications. This issue necessitates extensive calibration and multiple learning training samples, which can be time-consuming. To tackle this issue, we utilize the fine-tuning ability of CNN for extracting high-level features and the EA technique for data alignment. To enhance the efficacy of the transfer learning process, we adopt the fine-tuning technique, which involves retraining the CNN using the source dataset as the initial starting point. This is completed using one-third of the target dataset. Following this, the features that have been extracted by the CNN are implemented in an EA method to enhance similarity between the source and target feature distributions.