计算机科学
脑-机接口
边距(机器学习)
适应(眼睛)
人工智能
人工神经网络
脑电图
机器学习
模式识别(心理学)
校准
语音识别
统计
心理学
数学
物理
精神科
光学
作者
Christina Sartzetaki,Panagiotis Antoniadis,Nick Antonopoulos,Ioannis Gkinis,Agamemnon Krasoulis,Serafeim Perdikis,Vassilis Pitsikalis
标识
DOI:10.1109/smc53992.2023.10394335
摘要
There is a critical demand for BCI systems that can swiftly adapt to a new user and at the same time function with any user. We propose a fine-tuning approach for neural networks that serves a dual purpose; first, to minimize calibration times through requiring considerably less data - up to one-sixth - from the target subject than training from scratch, and second, to alleviate cases of user illiteracy by providing a substantial performance boost of over 11% in absolute accuracy from the features learned from other subjects. Ultimately, our adaptation method surpasses standard within-subject performance by a large margin in all subjects. We present ablation studies across three datasets, in which we demonstrate that fine-tuning outperforms other adaptation methods for BCI systems and that what matters most is the quantity of pre-training subjects, rather than their BCI-ability, achieving over 8% absolute increase in classification accuracy when scaling up the order of magnitude. Finally, we compare our approach to the state-of-the-art in EEG-based motor imagery and find it comparable, if not superior, to methods employing far more complex neural networks, obtaining 82.60% and 85.64% within-subject accuracy in the four-class BCIC IV-2a and binary MMI datasets respectively.
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