Review of Brain-inspired Computing Models for Human-Computer Interaction Based on Machine Learning and Deep Learning

핵심 개념

The author explores the evolution and application of brain-inspired computing models in human-computer interaction, focusing on decoding text and speech from brain signals using machine learning and deep learning.

초록

This review delves into the intersection of artificial intelligence with biomedicine, emphasizing brain-inspired computing models. It discusses the evolution of machine learning and deep learning models for human-computer interaction tasks, highlighting key technologies and challenges faced in brain signal decoding.

The content covers the importance of experimental design, EEG acquisition, eye-tracking acquisition, feature extraction algorithms, classification algorithms, and the utilization of public datasets like ZuCo. It also examines recent progress in EEG-to-text tasks using deep learning techniques to decode brain signals into text or speech.

Key points include the significance of stimuli control in experiments, the role of eye-tracking data in enhancing text decoding accuracy, and advancements in EEG-to-text translation models. The review showcases how researchers are bridging the gap between brain signals and natural language representations through innovative approaches.

통계

The model achieved a BLEU-1 score of 40.1% in EEG-to-text decoding.
An F1 score of 55.6% was achieved in zero-sample EEG-based ternary emotion classification.
DeWave scored 20.5 BLEU-1 and 29.5 Rouge-1 on the ZuCo dataset.

인용구

"The continuous development of artificial intelligence has a profound impact on biomedicine." - Content Source
"Researchers are constantly trying to understand neural mechanisms through studying the brain." - Content Source
"Brain-inspired computing is founded upon the structural framework and operational principles of the human brain." - Content Source

핵심 통찰 요약

Human-computer Interaction for Brain-inspired Computing Based on Machine Learning And Deep Learning

by Bihui Yu,Sib... 게시일 arxiv.org 03-11-2024

https://arxiv.org/pdf/2312.07213.pdf

더 깊은 질문

How can advancements in brain-inspired computing impact other industries beyond biomedicine?

Advancements in brain-inspired computing have the potential to revolutionize various industries beyond biomedicine. One significant area is human-computer interaction (HCI), where brain-inspired models based on machine learning and deep learning can enhance user experiences by enabling more intuitive and efficient interactions with technology. For example, in the field of virtual reality and augmented reality, brain-inspired computing could lead to more immersive and responsive experiences by interpreting users' cognitive states and intentions in real-time.
Moreover, advancements in brain-inspired computing can also benefit the fields of education and training. By understanding cognitive behavior through neural mechanisms, personalized learning systems can be developed that adapt to individual students' needs and preferences. This could lead to more effective educational tools that optimize knowledge retention and engagement.
Additionally, industries such as marketing and advertising could leverage brain-inspired computing to better understand consumer behavior and preferences. By analyzing neural responses to stimuli, companies can tailor their messaging and product offerings more effectively, leading to increased customer satisfaction and brand loyalty.
In essence, advancements in brain-inspired computing have far-reaching implications across various sectors by enhancing human-machine interactions, personalizing experiences, improving learning outcomes, optimizing marketing strategies, among many other applications.

How might understanding cognitive behavior through brain-inspired computing lead to breakthroughs in AI ethics?

Understanding cognitive behavior through brain-inspired computing has the potential to drive breakthroughs in AI ethics by providing insights into how humans process information, make decisions, and interact with technology. By studying neural mechanisms underlying ethical decision-making processes within the human brain using advanced computational models inspired by neuroscience principles,
Researchers may gain a deeper understanding of moral reasoning patterns at a neurological level.
This knowledge could inform the development of AI algorithms that mimic ethical decision-making processes observed in humans.
By integrating these insights into AI systems,
Developers may create ethically aligned artificial agents capable of making morally sound choices when faced with complex situations.
Furthermore,
Brain-inspired models may help identify biases or unethical behaviors embedded within existing AI systems
And provide methods for mitigating these issues through algorithmic transparency,
Explainability features,
And bias detection mechanisms.
Ultimately,
Advances in understanding cognitive behavior through Brain-Inspired Computing
Could pave the way for developing Ethical Artificial Intelligence Systems
That prioritize fairness,
Transparency,
And accountability while upholding societal values
And promoting responsible use of AI technologies.

What counterarguments exist against relying solely on machine learning models for Brain signal decoding?

While machine learning models have shown great promise for decoding Brain signals,
There are several counterarguments against relying solely on these approaches:

Interpretability: Machine Learning Models often operate as "black boxes," meaning they lack transparency about how they arrive at specific conclusions or predictions based on input data

Limited Generalization: Machine Learning Models trained on specific datasets may struggle To generalize well To new scenarios or tasks outside their training domain

Data Bias: Machine Learning Models are susceptible To biases present In The training data used To develop them,

Which Can result In unfair Or inaccurate predictions When applied To diverse populations Or novel contexts

Lack Of Biological Plausibility: Some argue That purely data-driven approaches neglect fundamental principles Of neurobiology And cognition,

Limiting The ability Of These Models To accurately capture The complexity Of Brain function And mental processes

Overfitting: There Is A risk That Machine Learning Models May overfit Training data—capturing noise Or irrelevant patterns rather than true underlying relationships—leading To poor performance On unseen data

Ethical Concerns:
Relying solely On machine-learning-based Approaches For Brain signal decoding raises ethical concerns related To privacy violations,

Data security risks,
Potential misuse Of sensitive information,
And autonomy infringements If decisions Are made Solely Based On automated Algorithms Without Human oversight And intervention

Computational Resource Demands:
Training Complex Machine Learning Models For Brain Signal Decoding Requires substantial computational resources And energy consumption Which May Not Be Sustainable Or Accessible In all settings

Overall,
While machine-learning-based Approaches Have demonstrated success In Various Applications Including Brain signal decoding,
It Is important To consider These Counterarguments And limitations When designing Comprehensive Solutions That integrate Multiple methodologies And Considerations For More Robust And Ethical Practices