# Evaluation of convolutional neural networks using a large multi-subject P300 dataset

arXiv: https://arxiv.org/abs/2001.04225 · HTML: https://mlsanity.com/paper/2001.04225/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 10 Jan 2020.

## Built on by (most cited first, 2 of 2)

- [Fuzzy temporal convolutional neural networks in P300-based Brain-computer interface for smart home interaction](https://mlsanity.com/paper/2204.04338/) (2022)
- [A Contrastive Learning Based Convolutional Neural Network for ERP Brain-Computer Interfaces](https://mlsanity.com/paper/2407.04738/) (2024)

## Abstract (arXiv)

Deep neural networks (DNN) have been studied in various machine learning areas. For example, event-related potential (ERP) signal classification is a highly complex task potentially suitable for DNN as signal-to-noise ratio is low, and underlying spatial and temporal patterns display a large intra- and intersubject variability. Convolutional neural networks (CNN) have been compared with baseline traditional models, i.e. linear discriminant analysis (LDA) and support vector machines (SVM) for single trial classification using a large multi-subject publicly available P300 dataset of school-age children (138 males and 112 females). For single trial classification, classification accuracy stayed between 62% and 64% for all tested classification models. When applying the trained classification models to averaged trials, accuracy increased to 76-79% without significant differences among classification models. CNN did not prove superior to baseline for the tested dataset. Comparison with related literature, limitations and future directions are discussed.