Pith. sign in

REVIEW 2 cited by

MindBigData 2022 A Large Dataset of Brain Signals

Not yet reviewed by Pith; the record is open.

Re-run · record.json Download PDF Read on arXiv ↗

This paper has not been read by Pith yet. Machine review is queued; the pith claim, tier, and objections will appear here once it completes.

SPECIMEN: schema-true, not a live event

T0 review · schema-true

One-sentence machine reading of the paper's core claim.

pith:XXXXXXXX · record.json · timestamp

arxiv 2212.14746 v1 pith:EDP27FFK submitted 2022-12-27 eess.SP cs.CVcs.LGq-bio.NC

David Vivancos , Felix Cuesta This is my paper · ORCID
classification eess.SPcs.CVcs.LGq-bio.NC
keywords brainactivitiesdataseteverymindbigdatasignalstaskstechnology
verification ladder T0 review T1 audit T2 compute T3 formal
0 comments
read the original abstract

Understanding our brain is one of the most daunting tasks, one we cannot expect to complete without the use of technology. MindBigData aims to provide a comprehensive and updated dataset of brain signals related to a diverse set of human activities so it can inspire the use of machine learning algorithms as a benchmark of 'decoding' performance from raw brain activities into its corresponding (labels) mental (or physical) tasks. Using commercial of the self, EEG devices or custom ones built by us to explore the limits of the technology. We describe the data collection procedures for each of the sub datasets and with every headset used to capture them. Also, we report possible applications in the field of Brain Computer Interfaces or BCI that could impact the life of billions, in almost every sector like healthcare game changing use cases, industry or entertainment to name a few, at the end why not directly using our brains to 'disintermediate' senses, as the final HCI (Human-Computer Interaction) device? simply what we call the journey from Type to Touch to Talk to Think.

Discussion (0). Continue with ORCID to comment.

Forward citations

Cited by 2 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. Geometric Machine Learning on EEG Signals

    cs.LG 2025-02 reject novelty 5.0 of 10

    An EEG pipeline combining transformer-based denoising with graph Ricci flow and a GCN reports 0.97 accuracy for digit versus non-digit thought classification, but without baselines or code.

  2. Removing Neural Signal Artifacts with Autoencoder-Targeted Adversarial Transformers (AT-AT)

    cs.LG 2025-02 conditional novelty 5.0 of 10

    An autoencoder-gated adversarial transformer denoises EEG-EMG mixtures with reconstruction accuracy comparable to larger published models at a fraction of the model size.

Pith tools