脑-机接口
计算机科学
会话(web分析)
任务(项目管理)
接口(物质)
适应(眼睛)
可用性
人机交互
控制(管理)
人工智能
异步通信
领域(数学分析)
机器学习
匹配(统计)
脑电图
心理学
神经科学
计算机网络
数学分析
统计
数学
管理
气泡
最大气泡压力法
精神科
并行计算
万维网
经济
作者
Satyam Kumar,Hussein Alawieh,Frigyes Samuel Racz,Rawan Fakhreddine,José del R. Millán
出处
期刊:PNAS nexus
[Oxford University Press]
日期:2024-02-16
被引量:1
标识
DOI:10.1093/pnasnexus/pgae076
摘要
Subject training is crucial for acquiring brain-computer interface (BCI) control. Typically, this requires collecting user-specific calibration data due to high inter-subject neural variability that limits the usability of generic decoders. However, calibration is cumbersome and may produce inadequate data for building decoders, especially with naïve subjects. Here, we show that a decoder trained on the data of a single expert is readily transferrable to inexperienced users via domain adaptation techniques allowing calibration-free BCI training. We introduce two real-time frameworks, (i) Generic Recentering (GR) through unsupervised adaptation and (ii) Personally Assisted Recentering (PAR) that extends GR by employing supervised recalibration of the decoder parameters. We evaluated our frameworks on 18 healthy naïve subjects over five online sessions, who operated a customary synchronous bar task with continuous feedback and a more challenging car racing game with asynchronous control and discrete feedback. We show that along with improved task-oriented BCI performance in both tasks, our frameworks promoted subjects' ability to acquire individual BCI skills, as the initial neurophysiological control features of an expert subject evolved and became subject specific. Furthermore, those features were task-specific and were learned in parallel as participants practiced the two tasks in every session. Contrary to previous findings implying that supervised methods lead to improved online BCI control, we observed that longitudinal training coupled with unsupervised domain matching (GR) achieved similar performance to supervised recalibration (PAR). Therefore, our presented frameworks facilitate calibration-free BCIs and have immediate implications for broader populations-such as patients with neurological pathologies-who might struggle to provide suitable initial calibration data.
科研通智能强力驱动
Strongly Powered by AbleSci AI