Toward Reliable Asynchronous Brain–Computer Interfaces via Periodic–Aperiodic Feature Integration

Fuzhi Cao; Fulong Wang; Zhibang Yue; Xiangrui Kong; Ming Li; Miaowen Jiang; Shiqiang Zheng; Xunming Ji

doi:10.1016/j.eng.2026.08.025

Engineering ›› :202608025 DOI: 10.1016/j.eng.2026.08.025

research-article

Toward Reliable Asynchronous Brain–Computer Interfaces via Periodic–Aperiodic Feature Integration

Author information +

History +

PDF (5158KB)

Abstract

Asynchronous brain–computer interfaces (BCIs) enable users to initiate commands at will, offering a more natural and flexible interaction paradigm compared with conventional synchronous systems. However, accurately distinguishing between intentional control (IC) and non-control (NC) states remains a critical challenge, particularly in noninvasive steady-state visual evoked response (SSVER)-based BCIs. Existing approaches either rely on stimulus-locked periodic features or incorporate auxiliary physiological signals, but their performance is often limited by response variability, nonstationarity, and reduced practicality. In this study, we propose a unified framework that jointly leverages periodic and aperiodic features of neural signals (UFPAF) to enhance state recognition in asynchronous BCIs. Specifically, the proposed method integrates SSVER-based periodic features with aperiodic characteristics derived from the 1/f-like structure of the power spectrum, enabling a

Keywords

Asynchronous brain–computer interfaces / Steady-state visual evoked response (SSVER) / Control state detection / Aperiodic / Periodic

Cite this article

Download citation ▾

Fuzhi Cao, Fulong Wang, Zhibang Yue, Xiangrui Kong, Ming Li, Miaowen Jiang, Shiqiang Zheng, Xunming Ji. Toward Reliable Asynchronous Brain–Computer Interfaces via Periodic–Aperiodic Feature Integration. Engineering 202608025 DOI:10.1016/j.eng.2026.08.025

登录浏览全文

4963

注册一个新账户忘记密码

References

Publishing order | Descend order by publishing year | Descend order by cited within

[1]	Gao X, Wang Y, Chen X, Gao S . Interface, interaction, and intelligence in generalized brain—computer interfaces. Trends Cogn Sci 2021; 25(8): 671-84.

[2]	Edelman BJ, Johnson N, Sohrabpour A, Tong S, Thakor N, He B . Systems neuroengineering: understanding and interacting with the brain. Engineering 2015; 1(3): 292-308.

[3]	Xu M, He F, Jung TP, Gu X, Ming D . Current challenges for the practical application of electroencephalography—based brain—computer interfaces. Engineering 2021; 7(12): 1710—2.

[4]	Edelman BJ, Zhang S, Schalk G, Brunner P, Müller—Putz G, Guan C, et al. Non—invasive brain—computer interfaces: state of the art and trends. IEEE Rev Biomed Eng 2025; 18: 26-49.

[5]	Ai J, Meng J, Mai X, Zhu X . BCI control of a robotic arm based on SSVEP with moving stimuli for reach and grasp tasks. IEEE J Biomed Health Inform 2023; 27(8): 3818—29.

[6]	Biasiucci A, Leeb R, Iturrate I, Perdikis S, Al—Khodairy A, Corbet T, et al. Brain—actuated functional electrical stimulation elicits lasting arm motor recovery after stroke. Nat Commun 2018; 9(1): 2421.

[7]	Zhou Z, Zhang L, Wei S, Zhang X, Mao L . Development and evaluation of BCI for operating VR flight simulator based on desktop VR equipment. Adv Eng Inform 2022; 51: 101499.

[8]	Li X, Chen J, Shi N, Yang C, Gao P, Chen X, et al. A hybrid steady—state visual evoked response—based brain—computer interface with meg and eeg. Expert Syst Appl 2023; 223: 119736.

[9]	Zhang X, Wei W, Qiu S, Li X, Wang Y, He H . Enhancing SSVEP—based BCI performance via consensus information transfer among subjects. IEEE Trans Neural Netw Learn Syst 2025; 36(8): 13833-47.

[10]	Aggarwal S, Chugh N . Review of machine learning techniques for EEG based brain computer interface. Arch Comput Methods Eng 2022; 29(5): 3001-20.

[11]	Cui H, Chi X, Wang L, Chen X . A high—rate hybrid BCI system based on high—frequency SSVEP and sEMG. IEEE J Biomed Health Inform 2023; 27(12): 5688-98.

[12]	Gao Y, Lang G, Zhang C, Wu R, Zhu Y, Zhao Y, et al. Rehabilitation exoskeleton system with bidirectional virtual reality feedback training strategy. CAAI Trans Intell Technol 2025; 10(3): 728—37.

[13]	Liu S, Liu M, Zhang D, Ming Z, Liu Z, Chen Q, et al. Brain—controlled hand exoskeleton based on augmented reality—fused stimulus paradigm. IEEE J Biomed Health Inform 2025; 29(4): 2932-44.

[14]	Chae Y, Jeong J, Jo S . Toward brain—actuated humanoid robots: asynchronous direct control using an EEG—based BCI. IEEE Trans Robot 2012; 28(5): 1131-44.

[15]	Chai X, Zhang Z, Guan K, Lu Y, Liu G, Zhang T, et al. A hybrid BCI—controlled smart home system combining SSVEP and EMG for individuals with paralysis. Biomed Signal Process Control 2020; 56: 101687.

[16]	Zhou Y, He S, Huang Q, Li Y . A hybrid asynchronous brain—computer interface combining SSVEP and EOG signals. IEEE Trans Biomed Eng 2020; 67(10): 2881—92.

[17]	Zhu Y, Li Y, Lu J, Li P . A hybrid BCI based on SSVEP and EOG for robotic arm control. Front Neurorobot 2020; 14: 583641.

[18]	Ha J, Park S, Im CH, Kim L . A hybrid brain—computer interface for real—life meal—assist robot control. Sensors 2021; 21(13): 4578.

[19]	Han X, Lin K, Gao S, Gao X . A novel system of SSVEP—based human—robot coordination. J Neural Eng 2019; 16(1): 016006.

[20]	Xia B, Li X, Xie H, Yang W, Li J, He L . Asynchronous brain—computer interface based on steady—state visual—evoked potential. Cognit Comput 2013; 5(2): 243-51.

[21]	Zhang D, Huang B, Wu W, Li S . An idle—state detection algorithm for SSVEP—based brain—computer interfaces using a maximum evoked response spatial filter. Int J Neural Syst 2015; 25(7): 1550030.

[22]	Suefusa K, Tanaka T . Asynchronous brain—computer interfacing based on mixed—coded visual stimuli. IEEE Trans Biomed Eng 2018; 65(9): 2119-29.

[23]	Ke Y, Du J, Liu S, Ming D . Enhancing detection of control state for high—speed asynchronous SSVEP—BCIs using frequency—specific framework. IEEE Trans Neural Syst Rehabil Eng 2023; 31: 1405-17.

[24]	Yang C, Yan X, Wang Y, Chen Y, Zhang H, Gao X . Spatio—temporal equalization multi—window algorithm for asynchronous SSVEP—based BCI. J Neural Eng 2021; 18(4): 0460b7.

[25]	Wang J, Cui Y, Zhang H, Wu H, Yang C . An asynchronous training—free SSVEP—BCI detection algorithm for non—equal prior probability scenarios. IEEE Trans Neural Syst Rehabil Eng 2024; 32: 4120—30.

[26]	Zhao J, Shi Y, Liu W, Zhou T, Li Z, Li X . A hybrid method fusing frequency recognition with attention detection to enhance an asynchronous brain—computer interface. IEEE Trans Neural Syst Rehabil Eng 2023; 31: 2391—8.

[27]	Zhao J, Li J, Wang X, Zhang Q, Li Z, Liang Z . Discriminating brainwave patterns of different control and non—control states for enhancing asynchronous brain—computer interfaces. Expert Syst Appl 2024; 252: 124145.

[28]	Donoghue T, Haller M, Peterson EJ, Varma P, Sebastian P, Gao R, et al. Parameterizing neural power spectra into periodic and aperiodic components. Nat Neurosci 2020; 23(12): 1655—65.

[29]	Wen H, Liu Z . Separating fractal and oscillatory components in the power spectrum of neurophysiological signal. Brain Topogr 2016; 29(1): 13-26.

[30]	Chen X, Wang Y, Gao S, Jung TP, Gao X . Filter bank canonical correlation analysis for implementing a high—speed SSVEP—based brain—computer interface. J Neural Eng 2015; 12(4): 046008.

[31]	Nakanishi M, Wang Y, Chen X, Wang YT, Gao X, Jung TP . Enhancing detection of SSVEPs for a high—speed brain speller using task—related component analysis. IEEE Trans Biomed Eng 2018; 65(1): 104—12.

[32]	Kwak NS, Müller KR, Lee SW . A lower limb exoskeleton control system based on steady state visual evoked potentials. J Neural Eng 2015; 12(5): 056009.

[33]	Wu H, Tu Y, Wu D . Asynchronous SSVEP—BCI recognition via multi—start—point slice ensembles and hard voting. In: 2026 IEEE International Conference on Acoustics, Speech and Signal Processing; 2026 May 4—8; Barcelona, Spain. New York City: IEEE; 2026. p. 7387-91.

[34]	Zhang X, Xu G, Mou X, Ravi A, Li M, Wang Y, et al. A convolutional neural network for the detection of asynchronous steady state motion visual evoked potential. IEEE Trans Neural Syst Rehabil Eng 2019; 27(6): 1303—11.

[35]	Hart PE, Nilsson NJ, Raphael B . A formal basis for the heuristic determination of minimum cost paths. IEEE Trans Syst Sci Cybern 1968; 4(2): 100-7.

[36]	Boto E, Holmes N, Leggett J, Roberts G, Shah V, Meyer SS, et al. Moving magnetoencephalography towards real—world applications with a wearable system. Nature 2018; 555(7698): 657—61.

[37]	Kluger DS, Forster C, Abbasi O, Chalas N, Villringer A, Gross J . Modulatory dynamics of periodic and aperiodic activity in respiration—brain coupling. Nat Commun 2023; 14(1): 4699.

[38]	Wang F, Cao F, Gao J, An N, Yang J, Wang Y, et al. Exploring the potential of SSVER—BCI based on contactless measurement using optically pumped magnetometers. IEEE J Biomed Health Inform 2025; PP: 1-12.

[39]	Liu B, Chen X, Shi N, Wang Y, Gao S, Gao X . Improving the performance of individually calibrated SSVEP—BCI by task—discriminant component analysis. IEEE Trans Neural Syst Rehabil Eng 2021; 29: 1998-2007.

[40]	Wang F, Cao F, Yang J, An N, Jiang M, Zheng S, et al. Enhancing individual calibration classification in SSVER—based BCI with exactly periodic component analysis. IEEE Trans Industr Inform 2026; 22(5): 1-12.

[41]	Lee JY, Lee S, Mishra A, Yan X, McMahan B, Gaisford B, et al. Brain—computer interface control with artificial intelligence copilots. Nat Mach Intell 2025; 7(9): 1510-23.