Detecting intention to execute the next movement while performing current movement from EEG using global optimal constrained ICA
Abstract
Background Brain-computer interfaces (BCIs) are a promising tool in neurorehabilitation. The intention to perform a motor action can be detected from brain signals and used to control robotic devices. Most previous studies have focused on the starting of movements from a resting state, while in daily life activities, motions occur continuously and the neural activities correlated to the evolving movements are yet to be investigated.Method First we investigate the existence of neural correlates of intention to replace an object on the table during a holding phase. Next, we present a new method to extract the movement-related cortical potentials (MRCP) from a single-trial EEG. A novel method called Global optimal constrained ICA (GocICA) is proposed to overcome the limitations of cICA which is implemented based on Particle Swarm Optimization (PSO) and Charged System Search (CSS) techniques. GocICA is then utilized for decoding the intention to grasp and lift and intention to replace movements where the results were compared.Results It was found that GocICA significantly improves the intention detection performance. Best results in offline detection were obtained with CSS-cICA for both kinds of intentions. Furthermore, pseudo-online decoding showed that GocICA was able to predict both intentions before the onset of related movements with the highest probability.Conclusions Decoding of the next movement intention during current movement is possible, which can be used to create more natural neuroprostheses. The results demonstrate that GocICA is a promising new algorithm for single-trial MRCP detection which can be used for detecting other types of ERPs such as P300.
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Independent Component Analysis (ICA) performs blind source separation extracting statistically independent components, isolates noise sources from physiological signals, and is used for EEG denoising and motor imagery feature extraction.
“| Independent Component | Blind source separation extracting | Isolate noise sources from | EEG denoising and motor imagery feature |”
From Recent advances in intelligent wearable systems: from multiscale biomechanical features towards human motion intent prediction (2026) · cites this paper as [258]
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- Authors
- Eilbeigi, E., & Setarehdan, S. K.
- Affiliations
- University of Tehran (IR)
- Corresponding author
- Setarehdan, Seyed Kamaledin
- Venue
- Computers in Biology and Medicine
- Publisher
- Elsevier BV
- Published
- 2018-08
- Volume
- 99
- Pages
- 63-75
- ISSN
- 0010-4825 (ISSN-L)
- OpenAlex
- W2804555462
- Cited by
- 7
- References
- 43
- Citations by year
- 2026: 1 · 2021: 3 · 2020: 2 · 2019: 1
- FWCI
- 0.89
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- Computers in Biology and Medicine (publishedVersion)
- PubMed (publishedVersion)
- Topics
- Life Sciences › Neuroscience › Cognitive Neuroscience › EEG and Brain-Computer Interfaces
- Physical Sciences › Engineering › Biomedical Engineering › Muscle activation and electromyography studies
- Life Sciences › Neuroscience › Cognitive Neuroscience › Free Will and Agency
- Keywords
- motion intention recognition, movement-related cortical potentials, single-trial EEG, independent component analysis, EEG source localization, object replacement, neural prostheses, brain-computer interface, neurorehabilitation, event-related potentials
- MeSH terms
- Adult, Algorithms, Brain, Electroencephalography, Evoked Potentials, Female, Humans, Male, Movement, Intention, Brain-Computer Interfaces
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