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Brain-inspired Computing Based on Deep Learning for Human-computer Interaction: A Review

arXiv 2312.07213 ↗First posted 12 Dec 2023cs.AIcited by 0

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Abstract (arXiv)

The continuous development of artificial intelligence has a profound impact on biomedicine and other fields, providing new research ideas and technical methods. Brain-inspired computing is an important intersection between multimodal technology and biomedical field. Focusing on the application scenarios of decoding text and speech from brain signals in human-computer interaction, this paper presents a comprehensive review of the brain-inspired computing models based on deep learning (DL), tracking its evolution, application value, challenges and potential research trends. We first reviews its basic concepts and development history, and divides its evolution into two stages: recent machine learning and current deep learning, emphasizing the importance of each stage in the research of brain-inspired computing for human-computer interaction. In addition, the latest progress of deep learning in different tasks of brain-inspired computing for human-computer interaction is reviewed from five perspectives, including datasets and different brain signals, and the application of key technologies in the model is elaborated in detail. Despite significant advances in brain-inspired computational models, challenges remain to fully exploit their capabilities, and we provide insights into possible directions for future academic research. For more detailed information, please visit our GitHub page: https://github.com/ultracoolHub/brain-inspired-computing.

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References · 23

  1. 2017Attention Is All You Needcited by 60,361
  2. 2021Learning Transferable Visual Models From Natural Language Supervisioncited by 33,744
  3. 2020wav2vec 2.0: A Framework for Self-Supervised Learning of Speech Representationscited by 3,905
  4. 2018Deep contextualized word representationscited by 2,290
  5. 2019Parallel WaveGAN: A fast waveform generation model based on generative adversarial networks with multi-resolution spectrogramcited by 340
  6. 2022Decoding speech perception from non-invasive brain recordingscited by 130
  7. 2021Open Vocabulary Electroencephalography-To-Text Decoding and Zero-shot Sentiment Classificationcited by 67
  8. 2022Toward a realistic model of speech processing in the brain with self-supervised learningcited by 63
  9. 2022SPD domain-specific batch normalization to crack interpretable unsupervised domain adaptation in EEGcited by 53
  10. 2021Brain-inspired computing: We need a master plancited by 51
  11. 2022MAtt: A Manifold Attention Network for EEG Decodingcited by 44
  12. 2022BrainCog: A Spiking Neural Network based Brain-inspired Cognitive Intelligence Engine for Brain-inspired AI and Brain Simulationcited by 33
11 more
  1. 2021Positive-Negative Momentum: Manipulating Stochastic Gradient Noise to Improve Generalizationcited by 28
  2. 2020EEG-based Brain-Computer Interfaces (BCIs): A Survey of Recent Studies on Signal Sensing Technologies and Computational Intelligence Approaches and their Applicationscited by 21
  3. 2020Spiking Neural Networks Hardware Implementations and Challenges: a Surveycited by 18
  4. 2022Evaluating Latent Space Robustness and Uncertainty of EEG-ML Models under Realistic Distribution Shiftscited by 16
  5. 2021EEG-Transformer: Self-attention from Transformer Architecture for Decoding EEG of Imagined Speechcited by 14
  6. 2019Brain2Char: A Deep Architecture for Decoding Text from Brain Recordingscited by 7
  7. 2022Impact of dataset size and long-term ECoG-based BCI usage on deep learning decoders performancecited by 3
  8. 2018Cross-Subject Transfer Learning Improves the Practicality of Real-World Applications of Brain-Computer Interfacescited by 2
  9. 2022Deep learning for ECoG brain-computer interface: end-to-end vs. hand-crafted featurescited by 2
  10. 2016A Survey of Brain Inspired Technologies for Engineeringcited by 1
  11. 2023Simple method for detecting sleep episodes in rats ECoG using machine learningcited by 1

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