## 4 Experiment and Results  
### ECA-ATCNet on MI-EEG Classification  
#### 4.1.1 Datasets and Parameter Setup  
The BCI IV-2a and PhysioNet MI-EEG datasets [58] were used for training and evaluating the proposed model. BCI-2a is a well-known public MI-EEG dataset created by Graz University of Technology in 2008. BCI-2a has been widely used in the research community and is thus regarded as a benchmark dataset for MI-EEG decoding. The dataset contains 9 subjects, who are instructed to perform 4 different motor imagery tasks: left-hand movement, right-hand movement, foot movement, and tongue movement. The BCI-2a dataset contains 5184 MI-EEG data trials (samples), recorded using 22 EEG electrodes from 9 subjects (576 trials per subject). Each MI trial lasts for 4 s, with a sampling rate of 250 Hz and a filter range of 0.5 to 100 Hz. For each subject, two sessions were recorded on different days, with each session containing 288 trials per subject. The BCI IV-2a dataset has been partitioned in a subject-independent manner. In the within-subject classification task, no additional dataset partitioning is required. One session(T) is used for training the model, while the other session(E) is used for evaluation.  
The PhysioNet MI-EEG dataset [59] records 64-channel EEG using the BCI2000 system. A total of 109 subjects each performed 84 trials, including four types of MI tasks: left fist, right fist, both fists, and feet. Each MI task includes 21 trials. Each trial lasts for 4 s, with a signal sampling rate of 160 Hz, meaning each trial contains 640 time points. The signals from 18 electrodes near the motor cortex (C1-C6, CP1-CP6, P1-P6) [30; 60] were used in model training and testing. In the between-subject classification task, 11-fold cross-validation is performed to evaluate the performance of the proposed model. During each validation, the data from 100 subjects are used to train the model, and the data from the remaining 10 subjects are used for evaluation, fully utilizing each subject's data while minimizing biases caused by individual differences, and ultimately calculating and reporting the overall average performance metrics.  
The BCI IV-2a and PhysioNet MI-EEG datasets record raw EEG data, consisting of time-series data from multiple electrode channels. During the data preprocessing, normalization is applied to each channel to reduce amplitude discrepancies between different channels. In the experiments, the model receives the normalized multi-channel time-series data and outputs the probability distribution for each category.  
Figure 12: The impact of different time steps on the Spiking–Softmax method’s simulation of the Softmax activation function. K denotes the time step, and MSE is the mean square error of the Spiking–Softmax method’s approximation to the Softmax activation function. The red cross indicates the chosen value of K in the given context, where K = 12 achieves the lowest MSE of 0.013991.  
The proposed ECA-ATCNet model has 0.44 Mb of parameters. Both the training and testing of the model were performed on two GPUs (Nvidia GTX 4090 24 GB, Santa Clara, CA, USA) and implemented using the TensorFlow framework. For all experiments, we used the following training configuration: Glorot uniform initializer for weight initialization, Adam optimizer, a learning rate of 0.0009, a batch size of 64, categorical cross-entropy as the loss function, 1000 epochs of training, and early stopping with a patience value of 300. These hyperparameters were determined through multiple experiments to help the model generalize well.