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Related Experiment Video

Updated: Jul 5, 2026

A Single-Channel and Non-Invasive Wearable Brain-Computer Interface for Industry and Healthcare
06:34

A Single-Channel and Non-Invasive Wearable Brain-Computer Interface for Industry and Healthcare

Published on: July 7, 2023

Overcoming brain non-stationarity: adaptive RLS classification for stable BCIs based on auditory evoked potentials.

Dovilė Kurmanavičiūtė1, Matilda Makkonen1, Ivan Zubarev1

  • 1Department of Neuroscience and Biomedical Engineering, Aalto University, P.O. Box 12200, FI-00076 Aalto, Finland.

Journal of Neural Engineering
|July 3, 2026
PubMed
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Adaptive recalibration significantly improves brain-computer interface (BCI) performance for auditory attention decoding, overcoming brain-state non-stationarity. This adaptive approach enhances real-time communication restoration for individuals with disabilities.

Area of Science:

  • Neuroscience
  • Biomedical Engineering
  • Signal Processing

Background:

  • Brain-computer interfaces (BCIs) aim to restore communication by decoding selective attention from auditory evoked potentials.
  • Clinical translation of auditory BCIs is challenged by brain-state non-stationarity, which degrades decoding performance.

Purpose of the Study:

  • To evaluate the impact of different classification strategies on auditory BCI performance under various evaluation settings.
  • To assess the effectiveness of adaptive recalibration in mitigating performance decline due to brain-state non-stationarity.

Main Methods:

  • Compared offline, causal, static real-time, and adaptive real-time (Recursive Least Squares - RLS) classifiers using 62-channel EEG data from 25 healthy adults.
  • Evaluated classifiers in offline (5-fold cross-validation), causal (20/80 split), simulated real-time (static and adaptive), and leave-one-subject-out (LOSO) settings.
Keywords:
BCIRLSauditorybrain–computer interfaceinformation transfer ratenon-stationarityrecursive least squares

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Assessment and Communication for People with Disorders of Consciousness
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Last Updated: Jul 5, 2026

A Single-Channel and Non-Invasive Wearable Brain-Computer Interface for Industry and Healthcare
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A Single-Channel and Non-Invasive Wearable Brain-Computer Interface for Industry and Healthcare

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Assessment and Communication for People with Disorders of Consciousness
07:37

Assessment and Communication for People with Disorders of Consciousness

Published on: August 1, 2017

Main Results:

  • Offline classifiers achieved a peak ROC AUC of 0.75. Causal and static real-time classifiers showed performance degradation (ROC AUC 0.63 and 0.51, respectively).
  • Adaptive RLS classifier significantly improved real-time performance, achieving ROC AUC 0.68 and ITR 1.42 bits/min (p < 0.001).
  • LOSO evaluation demonstrated RLS feasibility for zero-calibration deployment with trial-by-trial personalization (ROC AUC 0.57, ITR 0.86 bits/min).

Conclusions:

  • Brain-state non-stationarity is a primary factor limiting auditory BCI performance.
  • Lightweight adaptive recalibration substantially restores real-time decoding performance.
  • Adaptive strategies support the translational potential of ERP-based communication paradigms for restoring communication.