## 4 Experiment and Results
### ECA-ATCNet on MI-EEG Classification
#### 4.1.5 Calculation of Energy Efficiency  
To intuitively compare the energy consumption of ANNs models and SNNs models obtained through SIT-conversion, we used the energy estimation method proposed \begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline  & \multicolumn{3}{c}{**ECA-ATCNet**} & \multicolumn{3}{c}{**ATCNet**} & \multicolumn{3}{c}{**EEG-TCNet**} & \multicolumn{3}{c}{**D-ATCNet**} & \multicolumn{3}{c}{**CSANet**} \\  & \multicolumn{3}{c}{**(Proposed)**} & \multicolumn{3}{c}{**ATCNet**} & \multicolumn{3}{c}{**EEG-TCNet**} & \multicolumn{3}{c}{**D-ATCNet**} & \multicolumn{3}{c}{**CSANet**} \\ \hline
**Individual** & **Acc (\%)** & **\(\kappa\)** & **Acc (\%)** & **\(\kappa\)** & **Acc (\%)** & **\(\kappa\)** & **Acc (\%)** & **\(\kappa\)** & **Acc (\%)** & **\(\kappa\)** \\ \hline
1 & **92.5**1  & 0.90 & 88.5 & 0.85 & 89.3 & 0.86 & 88.9 & 0.85 & 86.1 & 0.81 \\ \hline
2 & **83.0**1  & 0.77 & 70.5 & 0.61 & 72.4 & 0.63 & 72.6 & 0.63 & 70.5 & 0.61 \\ \hline
3 & 97.2 & 0.96 & 97.6 & 0.97 & 97.4 & 0.97 & **97.9**1  & 0.97 & 95.1 & 0.94 \\ \hline
4 & 79.2 & 0.72 & 81.0 & 0.75 & 75.9 & 0.68 & **86.1**1  & 0.81 & 80.6 & 0.61 \\ \hline
5 & **89.4**1  & 0.86 & 83.0 & 0.77 & 83.7 & 0.78 & 83.0 & 0.77 & 84.3 & 0.79 \\ \hline
6 & **84.9**1  & 0.80 & 73.6 & 0.65 & 70.7 & 0.61 & 78.5 & 0.71 & 69.7 & 0.65 \\ \hline
7 & 79.7 & 0.73 & 93.1 & 0.91 & 93.1 & 0.91 & **95.5**1  & 0.94 & 94.1 & 0.92 \\ \hline
8 & **93.2**1  & 0.91 & 90.3 & 0.87 & 86.7 & 0.82 & 89.2 & 0.86 & 89.9 & 0.87 \\ \hline
9 & 91.8 & 0.89 & 91.0 & 0.88 & 85.2 & 0.80 & **92.0**1  & 0.89 & 93.7 & 0.92 \\ \hline Average & **87.9**1  & 0.84 & 85.4 & 0.81 & 83.8 & 0.78 & 87.1 & 0.83 & 84.1 & 0.78 \\ \hline SD & **6.05**1  & 0.08 & 9.10 & 0.12 & 9.20 & 0.12 & 8.08 & 0.11 & 9.11 & 0.13 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Comparison of ECA-ATCNet with other models in the within-subject classification task on the BCI-2a dataset for nine subjects. SD represents the standard deviation. \begin{table}
\begin{tabular}{c c c} \hline \hline
**References** & **Method** & **Accuracy** \\ \hline [PERSON] et al. [62] & ConTraNet & 65.55 \\ [PERSON] et al. [50] & EEGNet & 68.20 \\ [PERSON] et al. [33] & CSANet & 70.56 \\ This work & ECA-ATCNet & **71.88**1  \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Comparison of ECA-ATCNet with state-of-the-art models in the between-subject classification task on the PhysioNet MI-EEG dataset. by [63] to quantitatively calculate energy reduction. First, we quantified the computational complexity of the 1 layers of both models using floating-point operations (FLOPs). \[\text{FLOPs}_{\text{ANN}}(\text{l})=\left\{ \begin{array}{l}\text{k}^{2}\times \text{c}_{\text{in}}\times\text{c}_{\text{out}}\times\text{w}_{\text{in}}\times \text{h}_{\text{out}},\text{ Conv layer}\\ \text{f}_{\text{in}}\times\text{f}_{\text{out}},\text{\quad Linear layer}\end{array} \right. \tag{20}\]  
\[\text{FLOPs}_{\text{SNN}}(\text{l})=\text{FLOPs}_{\text{ANN}}(\text{l}) \times\frac{\text{SpikeNum}(\text{l})}{\text{NeuronNum}(\text{l})} \tag{21}\]  
where k denotes the size of the convolutional kernel, \(\text{c}_{\text{in}}\) and \(\text{c}_{\text{out}}\) denote the numbers of input and output channels, \(\text{w}_{\text{in}}\) and \(\text{h}_{\text{out}}\) denote the widths and heights of the output feature maps, and \(\text{f}_{\text{in}}\) and \(\text{f}_{\text{out}}\) are the numbers of input and output features. SpikeNum(l) and NeuronNum(l) denote the total number of spikes and the number of neurons in layer 1 across all time steps. \(\text{FLOPs}_{\text{ANN}}(\text{l})\) and \(\text{FLOPs}_{\text{SNN}}(\text{l})\) denote the computational complexity of layer 1 for ANN and SNN models, respectively. In ANNs, each operation involves a floating-point multiply-accumulate (MAC) operation, whereas, in SNNs, each operation is simply a floating-point addition (AC). In 45 nm CMOS, the energy cost of an ANN MAC operation is 4.6 pJ, while the energy cost of an SNN addition operation is only 0.9 pJ [64].