An Asynchronous Neuromorphic Architecture for Wearable EEG Emotion Recognition

Clicks: 4
ID: 338195
2026
Free PDF
Article Quality & Performance Metrics
Overall Quality
Not rated
Combines reader engagement with the AI quality analysis. This article has not been analysed, so there is no overall score — reader engagement is measured and shown alongside.
AI Quality Assessment
Not analyzed
Readership in this journal
Emerging

Ranked #416 of 416 articles by views in national science review

Most read Least read

Bar heights use a square-root scale. Only the 120 most-read articles are drawn; the journal has 416 in total.

Mint this article as an NFT
Not yet minted

Create a permanent, verifiable on-chain record of this article on the Scimatic Network. The NFT is held in your Journament account, and you can withdraw it to your own wallet at any time.

5 SUSD one-off · no wallet required
Sign in to mint
Abstract
Abstract Neural activity associated with emotion is event-driven and temporally sparse, whereas electroencephalography (EEG) is acquired as a continuous signal rather than a usable event stream. Conventional synchronous hardware processes all time steps densely, increasing energy costs during prolonged wearable monitoring. We propose an asynchronous neuromorphic architecture in which a dynamic adaptive spiking neural network converts features derived from EEG into sparse spike events. The model combines a depthwise separable gating module, division-free time-to-first-spike encoding, and adaptive temporal window regulation. The resulting events drive a globally asynchronous locally synchronous (GALS) accelerator that activates data movement and computation only for valid events, reducing switching during idle periods. DA-SNN outperforms lightweight models on SEED, SEED-IV, SEED-V, DEAP, and DREAMER. The accelerator completes inference on a preprocessed EEG tensor in 18 μs, with total power consumption of 148 mW and energy consumption of 2.66 μJ per inference.
Reference Key
openalex_W7225079293 Use this key to autocite in the manuscript while using SciMatic Manuscript Manager or Thesis Manager
Authors Jingxin Liu, Yiming Zhang, Zikai Song, Zixuan Lai, Junyi Liu, Fuze Tian, Koo Hyoung Lee, Qunxi Dong, Lixian Zhu, Bin Hu
Are you one of these authors? Claim your profile
Journal national science review
Year 2026
DOI
10.1093/nsr/nwag673
URL
https://doi.org/10.1093/nsr/nwag673
Keywords Keywords not found

Citations

No citations found. To add a citation, contact the admin at info@scimatic.org

Comments

No comments yet. Be the first to comment on this article.