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Affective computing for human-machine interaction via a bionic organic memristor exhibiting selective in situ
Bingjie Guo1, Xiaolong Zhong2, Zhe Yu3
1School of Chemistry and Chemical Engineering, Shanghai Jiao Tong University, Shanghai 200240, China. gang.liu@sjtu.edu.cn.
Materials Horizons
|July 2, 2024
Summary
Researchers developed a bionic organic memristor inspired by ion channels for faster, low-power affective computing. This novel device enables accurate emotion recognition from electroencephalography (EEG) data with near-sensor processing.
Area of Science:
- Materials Science
- Neuroscience
- Computer Science
Background:
- Affective computing faces challenges with data transmission speed and power consumption.
- Human-machine interaction requires efficient processing of complex biological signals.
Purpose of the Study:
- To develop a novel bionic organic memristor for near-sensor affective computing.
- To mimic ligand-gated ion channels (LGICs) for improved electroencephalography (EEG) signal processing.
- To reduce energy consumption and increase speed in affective computing applications.
Main Methods:
- Fabrication of a bionic organic memristor using a cobalt-ion and benzothiadiazole (Co-BTA) coordination polymer.
- Investigation of selective redox reaction sites through advanced characterization and theoretical calculations.
- Development of a near-sensor system for emotion recognition using the bionic memristor network.
Main Results:
- The Co-BTA memristor demonstrated selective activation mimicking LGICs.
- Achieved high reliability (200,000 cycles), integration (2^10 pixels), low energy consumption (4.05 pJ), and fast switching (94 ns).
- The near-sensor system attained over 95% accuracy in emotion recognition from EEG data.
Conclusions:
- The bionic organic memristor offers a promising solution for efficient affective computing.
- This approach significantly advances the development of empathetic human-machine interaction.
- Presents a novel bionic strategy for next-generation electronic devices.
