Loading...

Enhancing EEG spellers through natural language processing

Yazdani, M ; Sharif University of Technology | 2023

0 Viewed
  1. Type of Document: Article
  2. DOI: 10.1109/ICBME61513.2023.10488654
  3. Publisher: Institute of Electrical and Electronics Engineers Inc , 2023
  4. Abstract:
  5. Electroencephalogram (EEG) spellers are a vital communication tool for people with severe motor impairments, allowing them to express themselves by choosing letters or symbols on a screen. However, current EEG spellers face challenges such as low speed, low accuracy, and the lack of a universal method. This paper presents a novel approach that integrates a visual EEG-based speller with a Natural Language Processing (NLP) model to overcome these limitations. We use a hybrid SSVEP-RSVP model and augment it with an NLP model to create a speller that can write meaningful sentences. We evaluate our method on a between-subject scheme with four subjects and show that it significantly improves the accuracy of the basic hybrid speller (average accuracy increase by 16%). This research could potentially transform the field of assistive technology, offering a more effective and accurate communication tool for disabled people. © 2023 IEEE
  6. Keywords:
  7. EEG spellers ; Hybrid method ; Motor disabilities ; Natural Language Processing (NLP) ; P300 ; Steady State Visual Evoked Potential (SSVEP)
  8. Source: 2023 30th National and 8th International Iranian Conference on Biomedical Engineering, ICBME 2023 ; 2023 , Pages 290-293 ; 979-835035973-2 (ISBN)
  9. URL: https://ieeexplore.ieee.org/document/10488654