###### Abstract  
The implementation of a brain-computer interface (BCI) using electroencephalography typically entails two phases: feature extraction and classification utilizing a classifier. Consequently, there are numerous disordered combinations of feature extraction and classification techniques that apply to each classification target and dataset. In this study, we employed a neural network as a classifier to address the versatility of the system in converting inputs of various forms into outputs of various forms. As a preprocessing step, we utilized a transposed convolution to augment the width of the convolution and the number of output features, which were then classified using a convolutional neural network (CNN). Our implementation of a simple CNN incorporating a transposed convolution in the initial layer allowed us to classify the BCI Competition IV Dataset 2a Motor Imagery Task data. Our findings indicate that our proposed method, which incorporates a two-dimensional CNN with a transposed convolution, outperforms the accuracy achieved without the transposed convolution. Additionally, the accuracy obtained was comparable to conventional optimal preprocessing methods, demonstrating the effectiveness of the transposed convolution as a potential alternative for BCI preprocessing.  
BCI competition IV; brain-computer interface; convolutional neural network; electroencephalogram; transposed convolution +
Footnote †: journal: Medical  
previously mentioned studies. As a result, a wide range of combinations of preprocessing and classification methods have been employed, leading to inconsistencies within each classification target. The effectiveness of the selected feature extraction and classification method is contingent on the data, which raises the question of whether the optimal method has been applied to BCI data.  
The implementation of neural networks has become increasingly popular in recent times, given their ability to convert inputs into outputs, making them suitable for any classification problem. Furthermore, the development of specialized processing hardware for neural networks has enabled their integration into small devices such as smartphones [9].  
The utilization of neural networks, including convolutional neural networks (CNN), may hold the potential for creating highly effective BCI using modern technology [10]. Several studies have leveraged the use of CNN in EEG classification algorithms for BCI research, including EEGNet [11]. The application of EEGNet to the BCI competition dataset results in accuracy comparable to that achieved by conventional methods. In contrast to conventional EEG classification methods, which often rely on feature extraction and classification, neural networks have the ability to convert any input into any output, thus eliminating the need for feature extraction and allowing the direct entry of EEG signals into the classification model.  
In this study, we propose a neural network with transposed convolution for EEG analysis. Transposed convolution is a type of convolution that performs the inverse operation of a normal convolution. It is used to extract components from the convolved data by decomposing them. In other words, our method includes processing and feature extraction similar to window analysis (such as the filter bank [6], wavelet [12], or short-time Fourier transform (STFT) [13]) that has been used in many conventional EEG analyses. We demonstrate that conventional feature extraction can be replaced by the proposed method. In addition, a high classification accuracy can be obtained without explicit feature extraction. The concept of the proposed method has been reported in our previous study [14]. Here, we further explore the effectiveness of the proposed method.