mlsanityData to 14 Oct 2026

EEGMobile: Enhancing Speed and Accuracy in EEG-Based Gaze Prediction with Advanced Mobile Architectures

arXiv 2408.03449 ↗First posted 6 Aug 2024eess.SP cs.AI cs.LGcited by 0

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Abstract (arXiv)

Electroencephalography (EEG) analysis is an important domain in the realm of Brain-Computer Interface (BCI) research. To ensure BCI devices are capable of providing practical applications in the real world, brain signal processing techniques must be fast, accurate, and resource-conscious to deliver low-latency neural analytics. This study presents a model that leverages a pre-trained MobileViT alongside Knowledge Distillation (KD) for EEG regression tasks. Our results showcase that this model is capable of performing at a level comparable (only 3% lower) to the previous State-Of-The-Art (SOTA) on the EEGEyeNet Absolute Position Task while being 33% faster and 60% smaller. Our research presents a cost-effective model applicable to resource-constrained devices and contributes to expanding future research on lightweight, mobile-friendly models for EEG regression.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 22 of its 65 references that are in the map. First posted on arXiv 6 Aug 2024.

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22 of 65 references.

References · 22

  1. 2020An Image is Worth 16x16 Words: Transformers for Image Recognition at Scalecited by 30,186
  2. 2015Distilling the Knowledge in a Neural Networkcited by 10,324
  3. 2018An Empirical Evaluation of Generic Convolutional and Recurrent Networks for Sequence Modelingcited by 1,476
  4. 2021Early Convolutions Help Transformers See Bettercited by 396
  5. 2022Separable Self-attention for Mobile Vision Transformerscited by 163
  6. 2019Deep learning-based electroencephalography analysis: a systematic reviewcited by 156
  7. 2023Machine Learning for Synthetic Data Generation: A Reviewcited by 120
  8. 2020EEG-TCNet: An Accurate Temporal Convolutional Network for Embedded Motor-Imagery Brain-Machine Interfacescited by 63
  9. 2020Machine Learning for Predicting Epileptic Seizures Using EEG Signals: A Reviewcited by 33
  10. 2021EEGEyeNet: a Simultaneous Electroencephalography and Eye-tracking Dataset and Benchmark for Eye Movement Predictioncited by 21
  11. 2020Transfer Learning for Human Activity Recognition using Representational Analysis of Neural Networkscited by 14
  12. 2023Successfully Applying Lottery Ticket Hypothesis to Diffusion Modelcited by 11
10 more
  1. 2022A Deep Learning Approach for the Segmentation of Electroencephalography Data in Eye Tracking Applicationscited by 7
  2. 2022Time Majority Voting, a PC-based EEG Classifier for Non-expert Userscited by 7
  3. 2023Trends in Machine Learning and Electroencephalogram (EEG): A Review for Undergraduate Researcherscited by 7
  4. 2023ViT2EEG: Leveraging Hybrid Pretrained Vision Transformers for EEG Datacited by 7
  5. 2022BrainActivity1: A Framework of EEG Data Collection and Machine Learning Analysis for College Studentscited by 6
  6. 2022Vector-Based Data Improves Left-Right Eye-Tracking Classifier Performance After a Covariate Distributional Shiftcited by 3
  7. 2023One step closer to EEG based eye trackingcited by 3
  8. 2023Electrode Clustering and Bandpass Analysis of EEG Data for Gaze Estimationcited by 2
  9. 2024Fusing Pretrained ViTs with TCNet for Enhanced EEG Regressioncited by 2
  10. 2023Two Heads are Better than One: A Bio-inspired Method for Improving Classification on EEG-ET Datacited by 1

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  1. 2022Enhancing Low-Density EEG-Based Brain-Computer Interfaces with Similarity-Keeping Knowledge Distillation
  2. 2024EEG-GPT: Exploring Capabilities of Large Language Models for EEG Classification and Interpretation
  3. 2024EEGMamba: Bidirectional State Space Model with Mixture of Experts for EEG Multi-task Classification
  4. 2026EEG-SpikeAgent: Agentic Closed-Loop Program Synthesis for Automated EEG Spike Detection
  5. 2022MI-BMInet: An Efficient Convolutional Neural Network for Motor Imagery Brain--Machine Interfaces with EEG Channel Selection
  6. 2026STEAM: A Spatio-TEmporal Alignment Mixture-of-Experts Model with Hierarchical Pre-training for EEG Decoding
  7. 2026What the LLM Should Not Say: Boundary-Aware Context Grounding for A Seven-Channel EEG Agent
  8. 2021Distilling EEG Representations via Capsules for Affective Computing