mlsanityData to 14 Oct 2026

Time Majority Voting, a PC-based EEG Classifier for Non-expert Users

arXiv 2207.12662 ↗First posted 26 Jul 2022cs.LG cs.HC eess.SPcited by 7

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Abstract (arXiv)

Using Machine Learning and Deep Learning to predict cognitive tasks from electroencephalography (EEG) signals is a rapidly advancing field in Brain-Computer Interfaces (BCI). In contrast to the fields of computer vision and natural language processing, the data amount of these trials is still rather tiny. Developing a PC-based machine learning technique to increase the participation of non-expert end-users could help solve this data collection issue. We created a novel algorithm for machine learning called Time Majority Voting (TMV). In our experiment, TMV performed better than cutting-edge algorithms. It can operate efficiently on personal computers for classification tasks involving the BCI. These interpretable data also assisted end-users and researchers in comprehending EEG tests better.

Key facts. Cited by 7 papers in the map (data through 14 Oct 2026). Builds on 10 of its 42 references that are in the map. First posted on arXiv 26 Jul 2022.

Builds on

10 of 42 references.

References · 10

  1. 2019Deep learning-based electroencephalography analysis: a systematic reviewcited by 156
  2. 2015Learning Representations from EEG with Deep Recurrent-Convolutional Neural Networkscited by 45
  3. 2021Hierarchical Consistency Regularized Mean Teacher for Semi-supervised 3D Left Atrium Segmentationcited by 21
  4. 2021EEGEyeNet: a Simultaneous Electroencephalography and Eye-tracking Dataset and Benchmark for Eye Movement Predictioncited by 21
  5. 2019BiRA-Net: Bilinear Attention Net for Diabetic Retinopathy Gradingcited by 15
  6. 2016Mental State Recognition via Wearable EEGcited by 8
  7. 2020Multi-Phase Cross-modal Learning for Noninvasive Gene Mutation Prediction in Hepatocellular Carcinomacited by 6
  8. 2020Sea-Net: Squeeze-And-Excitation Attention Net For Diabetic Retinopathy Gradingcited by 6
  9. 2020Statistical control for spatio-temporal MEG/EEG source imaging with desparsified multi-task Lassocited by 5
  10. 2022Adaptive Mean-Residue Loss for Robust Facial Age Estimationcited by 4

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Cited by year

Citing papers per year: 2023: 2; 2024: 5.

7 papers.

  1. 2023Trends in Machine Learning and Electroencephalogram (EEG): A Review for Undergraduate Researcherscited by 7
  2. 2023ViT2EEG: Leveraging Hybrid Pretrained Vision Transformers for EEG Datacited by 7
  3. 2024Fusing Pretrained ViTs with TCNet for Enhanced EEG Regressioncited by 2
  4. 2024AnoGAN for Tabular Data: A Novel Approach to Anomaly Detectioncited by 1
  5. 2024EEGMobile: Enhancing Speed and Accuracy in EEG-Based Gaze Prediction with Advanced Mobile Architecturescited by 0
  6. 2024Integrating HCI Datasets in Project-Based Machine Learning Courses: A College-Level Review and Case Studycited by 0
  7. 2024Effect of Kernel Size on CNN-Vision-Transformer-Based Gaze Prediction Using Electroencephalography Datacited by 0