# Object classification from randomized EEG trials

arXiv: https://arxiv.org/abs/2004.06046 · HTML: https://mlsanity.com/paper/2004.06046/

## Key facts

- Cited by 19 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 9 Apr 2020.

## Built on by (most cited first, 19 of 19)

- [Decoding Natural Images from EEG for Object Recognition](https://mlsanity.com/paper/2308.13234/) (2023)
- [Visual Decoding and Reconstruction via EEG Embeddings with Guided Diffusion](https://mlsanity.com/paper/2403.07721/) (2024)
- [Decoding Visual Neural Representations by Multimodal Learning of Brain-Visual-Linguistic Features](https://mlsanity.com/paper/2210.06756/) (2022)
- [Visual Neural Decoding via Improved Visual-EEG Semantic Consistency](https://mlsanity.com/paper/2408.06788/) (2024)
- [Neural-MCRL: Neural Multimodal Contrastive Representation Learning for EEG-based Visual Decoding](https://mlsanity.com/paper/2412.17337/) (2024)
- [Mind's Eye: Image Recognition by EEG via Multimodal Similarity-Keeping Contrastive Learning](https://mlsanity.com/paper/2406.16910/) (2024)
- [MindBigData 2022 A Large Dataset of Brain Signals](https://mlsanity.com/paper/2212.14746/) (2022)
- [Inter-subject Contrastive Learning for Subject Adaptive EEG-based Visual Recognition](https://mlsanity.com/paper/2202.02901/) (2022)
- [Source-free Subject Adaptation for EEG-based Visual Recognition](https://mlsanity.com/paper/2301.08448/) (2023)
- [See What You See: Self-supervised Cross-modal Retrieval of Visual Stimuli from Brain Activity](https://mlsanity.com/paper/2208.03666/) (2022)
- [Subject-Aware Multi-Granularity Alignment for Zero-Shot EEG-to-Image Retrieval](https://mlsanity.com/paper/2604.17782/) (2026)
- [Spatial-Functional awareness Transformer-based graph archetype contrastive learning for Decoding Visual Neural Representations from EEG](https://mlsanity.com/paper/2509.24761/) (2025)
- [EEG-based Image Feature Extraction for Visual Classification using Deep Learning](https://mlsanity.com/paper/2209.13090/) (2022)
- [Alljoined1 -- A dataset for EEG-to-Image decoding](https://mlsanity.com/paper/2404.05553/) (2024)
- [EIT-1M: One Million EEG-Image-Text Pairs for Human Visual-textual Recognition and More](https://mlsanity.com/paper/2407.01884/) (2024)
- [Self-Calibrating BCIs: Ranking and Recovery of Mental Targets Without Labels](https://mlsanity.com/paper/2506.11151/) (2025)
- [Overcoming the Domain Gap in Neural Action Representations](https://mlsanity.com/paper/2112.01176/) (2021)
- [The Repeated-Stimulus Confound in Electroencephalography](https://mlsanity.com/paper/2508.00531/) (2025)
- [Brain-Inspired Capture: Evidence-Driven Neuromimetic Perceptual Simulation for Visual Decoding](https://mlsanity.com/paper/2604.17927/) (2026)

## Abstract (arXiv)

New results suggest strong limits to the feasibility of classifying human brain activity evoked from image stimuli, as measured through EEG. Considerable prior work suffers from a confound between the stimulus class and the time since the start of the experiment. A prior attempt to avoid this confound using randomized trials was unable to achieve results above chance in a statistically significant fashion when the data sets were of the same size as the original experiments. Here, we again attempt to replicate these experiments with randomized trials on a far larger (20x) dataset of 1,000 stimulus presentations of each of forty classes, all from a single subject. To our knowledge, this is the largest such EEG data collection effort from a single subject and is at the bounds of feasibility. We obtain classification accuracy that is marginally above chance and above chance in a statistically significant fashion, and further assess how accuracy depends on the classifier used, the amount of training data used, and the number of classes. Reaching the limits of data collection without substantial improvement in classification accuracy suggests limits to the feasibility of this enterprise.