This paper presents WideConvNet, a novel convolutional neural network (ConvNet or CNN) that outperforms three established end-to-end models-EEGNet, Shallow Convolutional Neural Network (ShallowConvNet), and Deep Convolutional Neural Network (DeepConvNet)-in classifying event related potentials (ERPs) from electroencephalogram (EEG) signals. At the core of WideConvNet is an adapted inception module for 1D data, utilizing varying-size 1D filters inspired by the wavelet scattering transform, which are learned during the training phase to capture temporal patterns across different scales within the EEG signals. We evaluated the performance of WideConvNet on multichannel EEG data from 20 male subjects with alcoholic use disorder (AUD) and 20 healthy male controls, each providing 16 1.0-s ERPs. Using 10-fold leave-subjects-out cross-validation, WideConvNet demonstrated a significantly higher classification accuracy (𝒑 = 𝟎. 𝟎𝟎𝟒) at detecting AUD, and an area under the curve of the receiver operating characteristic (AUC-ROC, 𝒑 = 𝟎. 𝟎𝟎𝟒), compared to EEGNet and ShallowConvNet. WideConvNet also had a comparable performance to DeepConvNet but with 28% fewer parameters. These findings suggest that WideConvNet, specifically on short duration EEG signals, offers a balanced solution, combining robust classification performance with improved computational efficiency.