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

arXiv: https://arxiv.org/abs/2312.07213 · HTML: https://mlsanity.com/paper/2312.07213/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 23 of its 230 references that are in the map.
- First posted on arXiv 12 Dec 2023.

## Builds on (most cited first, 23 of 23)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Learning Transferable Visual Models From Natural Language Supervision](https://mlsanity.com/paper/2103.00020/) (2021)
- [wav2vec 2.0: A Framework for Self-Supervised Learning of Speech Representations](https://mlsanity.com/paper/2006.11477/) (2020)
- [Deep contextualized word representations](https://mlsanity.com/paper/1802.05365/) (2018)
- [Parallel WaveGAN: A fast waveform generation model based on generative adversarial networks with multi-resolution spectrogram](https://mlsanity.com/paper/1910.11480/) (2019)
- [Decoding speech perception from non-invasive brain recordings](https://mlsanity.com/paper/2208.12266/) (2022)
- [Open Vocabulary Electroencephalography-To-Text Decoding and Zero-shot Sentiment Classification](https://mlsanity.com/paper/2112.02690/) (2021)
- [Toward a realistic model of speech processing in the brain with self-supervised learning](https://mlsanity.com/paper/2206.01685/) (2022)
- [SPD domain-specific batch normalization to crack interpretable unsupervised domain adaptation in EEG](https://mlsanity.com/paper/2206.01323/) (2022)
- [Brain-inspired computing: We need a master plan](https://mlsanity.com/paper/2104.14517/) (2021)
- [MAtt: A Manifold Attention Network for EEG Decoding](https://mlsanity.com/paper/2210.01986/) (2022)
- [BrainCog: A Spiking Neural Network based Brain-inspired Cognitive Intelligence Engine for Brain-inspired AI and Brain Simulation](https://mlsanity.com/paper/2207.08533/) (2022)
- [Positive-Negative Momentum: Manipulating Stochastic Gradient Noise to Improve Generalization](https://mlsanity.com/paper/2103.17182/) (2021)
- [EEG-based Brain-Computer Interfaces (BCIs): A Survey of Recent Studies on Signal Sensing Technologies and Computational Intelligence Approaches and their Applications](https://mlsanity.com/paper/2001.11337/) (2020)
- [Spiking Neural Networks Hardware Implementations and Challenges: a Survey](https://mlsanity.com/paper/2005.01467/) (2020)
- [Evaluating Latent Space Robustness and Uncertainty of EEG-ML Models under Realistic Distribution Shifts](https://mlsanity.com/paper/2209.11233/) (2022)
- [EEG-Transformer: Self-attention from Transformer Architecture for Decoding EEG of Imagined Speech](https://mlsanity.com/paper/2112.09239/) (2021)
- [Brain2Char: A Deep Architecture for Decoding Text from Brain Recordings](https://mlsanity.com/paper/1909.01401/) (2019)
- [Impact of dataset size and long-term ECoG-based BCI usage on deep learning decoders performance](https://mlsanity.com/paper/2209.03789/) (2022)
- [Cross-Subject Transfer Learning Improves the Practicality of Real-World Applications of Brain-Computer Interfaces](https://mlsanity.com/paper/1810.02842/) (2018)
- [Deep learning for ECoG brain-computer interface: end-to-end vs. hand-crafted features](https://mlsanity.com/paper/2210.02544/) (2022)
- [A Survey of Brain Inspired Technologies for Engineering](https://mlsanity.com/paper/1610.09882/) (2016)
- [Simple method for detecting sleep episodes in rats ECoG using machine learning](https://mlsanity.com/paper/2302.00933/) (2023)

## 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.