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Are EEG-to-Text Models Working?

Hyejeong Jo, Yiqian Yang, Juhyeok Han, Yiqun Duan, Hui Xiong, Won Hee Lee

TL;DR

It is revealed that model performance on noise data can be comparable to that on EEG data, highlighting the need for stricter evaluation practices in EEG-to-Text research, emphasizing transparent reporting and rigorous benchmarking with noise inputs.

Abstract

This work critically analyzes existing models for open-vocabulary EEG-to-Text translation. We identify a crucial limitation: previous studies often employed implicit teacher-forcing during evaluation, artificially inflating performance metrics. Additionally, they lacked a critical benchmark - comparing model performance on pure noise inputs. We propose a methodology to differentiate between models that truly learn from EEG signals and those that simply memorize training data. Our analysis reveals that model performance on noise data can be comparable to that on EEG data. These findings highlight the need for stricter evaluation practices in EEG-to-Text research, emphasizing transparent reporting and rigorous benchmarking with noise inputs. This approach will lead to more reliable assessments of model capabilities and pave the way for robust EEG-to-Text communication systems. Code is available at https://github.com/NeuSpeech/EEG-To-Text

Are EEG-to-Text Models Working?

TL;DR

It is revealed that model performance on noise data can be comparable to that on EEG data, highlighting the need for stricter evaluation practices in EEG-to-Text research, emphasizing transparent reporting and rigorous benchmarking with noise inputs.

Abstract

This work critically analyzes existing models for open-vocabulary EEG-to-Text translation. We identify a crucial limitation: previous studies often employed implicit teacher-forcing during evaluation, artificially inflating performance metrics. Additionally, they lacked a critical benchmark - comparing model performance on pure noise inputs. We propose a methodology to differentiate between models that truly learn from EEG signals and those that simply memorize training data. Our analysis reveals that model performance on noise data can be comparable to that on EEG data. These findings highlight the need for stricter evaluation practices in EEG-to-Text research, emphasizing transparent reporting and rigorous benchmarking with noise inputs. This approach will lead to more reliable assessments of model capabilities and pave the way for robust EEG-to-Text communication systems. Code is available at https://github.com/NeuSpeech/EEG-To-Text
Paper Structure (10 sections, 1 figure, 4 tables)

This paper contains 10 sections, 1 figure, 4 tables.

Table of Contents

  1. Introduction
  2. Materials and Methods
  3. Task Definition
  4. ZuCo Datasets
  5. EEG-to-Text Decoding Evaluation
  6. EEG-to-Text Decoding
  7. Training
  8. Evaluation
  9. Results and Discussion
  10. Conclusion

Figures (1)

  • Figure 1: Schematic illustration of the pipeline for a comprehensive assessment of EEG-to-Text models across four distinct training and evaluation setups wang2022open_aaai_eeg2text. These setups explore various combinations of training with either EEG data or random noise as input, followed by evaluation on the same type of data. This approach reveals how models perform under different input conditions. Each setup is further divided to show the influence of teacher-forcing on text generation.