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Open AccessArticle

Electroencephalography-Based Brain–Computer Interface System Using Tongue Movement Imagery for Wheelchair Control

by Theerat Saichoo, Nannaphat Siribunyaphat, Bukhoree Sahoh, M. Arif Efendi and Yunyong Punsawad

Sensors 2026, 26(7), 2211; https://doi.org/10.3390/s26072211 - 2 Apr 2026

Viewed by 994

Abstract

Brain–computer interfaces (BCIs) are essential in assistive technologies to restore mobility in individuals with motor impairments. Although electroencephalography (EEG)-based brain-controlled wheelchairs have been extensively studied, most tongue-controlled systems rely on physical tongue movements, intraoral devices, or limited offline commands, which reduces the usability [...] Read more.

Brain–computer interfaces (BCIs) are essential in assistive technologies to restore mobility in individuals with motor impairments. Although electroencephalography (EEG)-based brain-controlled wheelchairs have been extensively studied, most tongue-controlled systems rely on physical tongue movements, intraoral devices, or limited offline commands, which reduces the usability and comfort. This study introduces an EEG-based tongue motor imagery (MI) BCI for intuitive and entirely mental wheelchair control. By leveraging preserved motor function and the cortical representation of the tongue, the system enables natural four-directional control through imagined tongue movements. Six imagined tongue actions—touching the left and right mouth corners, the upper and lower lips, and producing left and right cheek bulges—were designed to elicit alpha-band event-related desynchronization (ERD) patterns over the tongue motor cortex. EEG data were collected from 15 healthy participants using a 14-channel consumer-grade EMOTIV EPOC X headset. Alpha-band ERD features were extracted and classified using linear discriminant analysis, support vector machine, naïve Bayes, and artificial neural networks (ANNs). Simpler command sets yielded the highest accuracy: two-class tasks achieved 76.19%, while the performance decreased with increasing task complexity. The ANN achieved superior results in multi-class scenarios. The proposed tongue MI method offers initial support for developing a BCI control strategy for assistive technology; however, further improvements in classification techniques, user training, and real-time validation are needed to improve the robustness and practical usability. Full article

(This article belongs to the Special Issue Combining Brain-Computer Interfaces and Assistive Biosensing Technologies)

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33 pages, 14874 KB

Open AccessArticle

A Flash Group Creation Algorithm for P300 Brain–Computer Interface Integration with Irregular Assistive Technology Keyboard Layouts

by Jane E. Huggins, Palash Biswas, James K. Huggins and Rishabh Chandel

Sensors 2026, 26(7), 2123; https://doi.org/10.3390/s26072123 - 29 Mar 2026

Viewed by 1025

Abstract

An event-related potential (ERP)-based brain–computer interface (BCI), or P300 BCI, has long been intended for communication access for individuals with severe motor impairments. BCI access to communication tools, websites, and augmentative and alternative communication (AAC) keyboards requires aligning BCI stimuli to screens with [...] Read more.

An event-related potential (ERP)-based brain–computer interface (BCI), or P300 BCI, has long been intended for communication access for individuals with severe motor impairments. BCI access to communication tools, websites, and augmentative and alternative communication (AAC) keyboards requires aligning BCI stimuli to screens with differing numbers of various-sized keys in partially populated grid layouts. Six design priorities were defined for creating and ordering flash groups: identifiability, unpredictability, perceptibility, minimality, anti-adjacency, and equality. Building on the checkerboard paradigm, multiple algorithmic approaches were evaluated on simulated AAC screens to create the magic square paradigm (MSP) for flash group creation for irregular key layouts. The MSP algorithm was then used for BCI access to the dynamic screens of a commercial AAC device that combines text-based and icon-based language representations and the resulting flash groups analyzed for design priorities of anti-adjacency and equality. The 126,944 flash groups created for 5778 selections on AAC screens had 0 groups with side-by-side adjacency, 0.02% with adjacency to an amalgamated key, and 6% with diagonally adjacent keys. The average difference between the shortest and longest flash groups was 1.9 keys. The MSP provides a novel method to access dynamic AAC keyboards with irregular layouts and multiple key sizes. Full article

(This article belongs to the Special Issue Combining Brain-Computer Interfaces and Assistive Biosensing Technologies)

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15 pages, 1697 KB

Open AccessArticle

Online Compensation of Systematic Effects in Stimuli Generation for XR-Based SSVEP BCIs

by Leopoldo Angrisani, Egidio De Benedetto, Matteo D’Iorio, Luigi Duraccio, Fabrizio Lo Regio and Annarita Tedesco

Sensors 2026, 26(3), 766; https://doi.org/10.3390/s26030766 - 23 Jan 2026

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Abstract

Background: Brain–Computer Interfaces (BCIs) based on Steady-State Visually Evoked Potentials (SSVEPs) and Extended Reality (XR) offer promising solutions for highly wearable applications, but their classification performance can be affected by systematic effects in stimulus presentation. Novelty: This study introduces a novel [...] Read more.

Background: Brain–Computer Interfaces (BCIs) based on Steady-State Visually Evoked Potentials (SSVEPs) and Extended Reality (XR) offer promising solutions for highly wearable applications, but their classification performance can be affected by systematic effects in stimulus presentation. Novelty: This study introduces a novel online compensation method to compensate for systematic effects in the Refresh Rate (RR) of XR displays, enhancing SSVEP classification without requiring additional training or invasive measurements. Methods: A non-invasive monitoring module was incorporated into the developed BCI pipeline to measure frame rate variations in the XR display, allowing deviations between nominal RR and measured values to be automatically detected and compensated for. Classification performance was evaluated using Filter Bank Canonical Correlation Analysis (FBCCA). Statistical significance was assessed using Student’s t-test. Materials: Two datasets were used: a dataset based on Moverio BT-350, including 9 subjects, and a dataset based on HoloLens 2, including 30 subjects, all collected by the authors. Results: The proposed compensation method led to significant improvements in SSVEP classification accuracy, proportional to the magnitude of fps deviations. In some cases, classification accuracy increased by up to 300% relative to its original value. Statistical analyses confirmed the reliability of the results across subjects and datasets. Conclusions: These findings show that the proposed method effectively enhances SSVEP-based BCIs in XR environments and provides a robust foundation for practical applications requiring high reliability. Full article

(This article belongs to the Special Issue Combining Brain-Computer Interfaces and Assistive Biosensing Technologies)

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22 pages, 4200 KB

Open AccessArticle

Investigation of Personalized Visual Stimuli via Checkerboard Patterns Using Flickering Circles for SSVEP-Based BCI System

by Nannaphat Siribunyaphat, Natjamee Tohkhwan and Yunyong Punsawad

Sensors 2025, 25(15), 4623; https://doi.org/10.3390/s25154623 - 25 Jul 2025

Cited by 2 | Viewed by 3079

Abstract

In this study, we conducted two steady-state visual evoked potential (SSVEP) studies to develop a practical brain–computer interface (BCI) system for communication and control applications. The first study introduces a novel visual stimulus paradigm that combines checkerboard patterns with flickering circles configured in [...] Read more.

In this study, we conducted two steady-state visual evoked potential (SSVEP) studies to develop a practical brain–computer interface (BCI) system for communication and control applications. The first study introduces a novel visual stimulus paradigm that combines checkerboard patterns with flickering circles configured in single-, double-, and triple-layer forms. We tested three flickering frequency conditions: a single fundamental frequency, a combination of the fundamental frequency and its harmonics, and a combination of two fundamental frequencies. The second study utilizes personalized visual stimuli to enhance SSVEP responses. SSVEP detection was performed using power spectral density (PSD) analysis by employing Welch’s method and relative PSD to extract SSVEP features. Commands classification was carried out using a proposed decision rule–based algorithm. The results were compared with those of a conventional checkerboard pattern with flickering squares. The experimental findings indicate that single-layer flickering circle patterns exhibit comparable or improved performance when compared with the conventional stimuli, particularly when customized for individual users. Conversely, the multilayer patterns tended to increase visual fatigue. Furthermore, individualized stimuli achieved a classification accuracy of 90.2% in real-time SSVEP-based BCI systems for six-command generation tasks. The personalized visual stimuli can enhance user experience and system performance, thereby supporting the development of a practical SSVEP-based BCI system. Full article

(This article belongs to the Special Issue Combining Brain-Computer Interfaces and Assistive Biosensing Technologies)

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19 pages, 8391 KB

Open AccessEditor’s ChoiceArticle

NeuroFlex: Feasibility of EEG-Based Motor Imagery Control of a Soft Glove for Hand Rehabilitation

by Soroush Zare, Sameh I. Beaber and Ye Sun

Sensors 2025, 25(3), 610; https://doi.org/10.3390/s25030610 - 21 Jan 2025

Cited by 13 | Viewed by 7329

Abstract

Motor impairments resulting from neurological disorders, such as strokes or spinal cord injuries, often impair hand and finger mobility, restricting a person’s ability to grasp and perform fine motor tasks. Brain plasticity refers to the inherent capability of the central nervous system to [...] Read more.

Motor impairments resulting from neurological disorders, such as strokes or spinal cord injuries, often impair hand and finger mobility, restricting a person’s ability to grasp and perform fine motor tasks. Brain plasticity refers to the inherent capability of the central nervous system to functionally and structurally reorganize itself in response to stimulation, which underpins rehabilitation from brain injuries or strokes. Linking voluntary cortical activity with corresponding motor execution has been identified as effective in promoting adaptive plasticity. This study introduces NeuroFlex, a motion-intent-controlled soft robotic glove for hand rehabilitation. NeuroFlex utilizes a transformer-based deep learning (DL) architecture to decode motion intent from motor imagery (MI) EEG data and translate it into control inputs for the assistive glove. The glove’s soft, lightweight, and flexible design enables users to perform rehabilitation exercises involving fist formation and grasping movements, aligning with natural hand functions for fine motor practices. The results show that the accuracy of decoding the intent of fingers making a fist from MI EEG can reach up to

85.3 %

, with an average AUC of 0.88. NeuroFlex demonstrates the feasibility of detecting and assisting the patient’s attempted movements using pure thinking through a non-intrusive brain–computer interface (BCI). This EEG-based soft glove aims to enhance the effectiveness and user experience of rehabilitation protocols, providing the possibility of extending therapeutic opportunities outside clinical settings. Full article

(This article belongs to the Special Issue Combining Brain-Computer Interfaces and Assistive Biosensing Technologies)

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12 pages, 3303 KB

Open AccessArticle

Comparison of Subdural and Intracortical Recordings of Somatosensory Evoked Responses

by Felipe Rettore Andreis, Suzan Meijs, Thomas Gomes Nørgaard dos Santos Nielsen, Taha Al Muhamadee Janjua and Winnie Jensen

Sensors 2024, 24(21), 6847; https://doi.org/10.3390/s24216847 - 25 Oct 2024

Cited by 1 | Viewed by 2686

Abstract

Micro-electrocorticography (µECoG) electrodes have emerged to balance the trade-off between invasiveness and signal quality in brain recordings. However, its large-scale applicability is still hindered by a lack of comparative studies assessing the relationship between ECoG and traditional recording methods such as penetrating electrodes. [...] Read more.

Micro-electrocorticography (µECoG) electrodes have emerged to balance the trade-off between invasiveness and signal quality in brain recordings. However, its large-scale applicability is still hindered by a lack of comparative studies assessing the relationship between ECoG and traditional recording methods such as penetrating electrodes. This study aimed to compare somatosensory evoked potentials (SEPs) through the lenses of a µECoG and an intracortical microelectrode array (MEA). The electrodes were implanted in the pig’s primary somatosensory cortex, while SEPs were generated by applying electrical stimulation to the ulnar nerve. The SEP amplitude, signal-to-noise ratio (SNR), power spectral density (PSD), and correlation structure were analysed. Overall, SEPs resulting from MEA recordings had higher amplitudes and contained significantly more spectral power, especially at higher frequencies. However, the SNRs were similar between the interfaces. These results demonstrate the feasibility of using µECoG to decode SEPs with wide-range applications in physiology monitoring and brain–computer interfaces. Full article

(This article belongs to the Special Issue Combining Brain-Computer Interfaces and Assistive Biosensing Technologies)

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18 pages, 8360 KB

Open AccessArticle

A Method for the Spatial Interpolation of EEG Signals Based on the Bidirectional Long Short-Term Memory Network

by Wenlong Hu, Bowen Ji and Kunpeng Gao

Sensors 2024, 24(16), 5215; https://doi.org/10.3390/s24165215 - 12 Aug 2024

Cited by 2 | Viewed by 3764

Abstract

The precision of electroencephalograms (EEGs) significantly impacts the performance of brain–computer interfaces (BCI). Currently, the majority of research into BCI technology gives priority to lightweight design and a reduced electrode count to make it more suitable for application in wearable environments. This paper [...] Read more.

The precision of electroencephalograms (EEGs) significantly impacts the performance of brain–computer interfaces (BCI). Currently, the majority of research into BCI technology gives priority to lightweight design and a reduced electrode count to make it more suitable for application in wearable environments. This paper introduces a deep learning-based time series bidirectional (BiLSTM) network that is designed to capture the inherent characteristics of EEG channels obtained from neighboring electrodes. It aims to predict the EEG data time series and facilitate the conversion process from low-density EEG signals to high-density EEG signals. BiLSTM pays more attention to the dependencies in time series data rather than mathematical maps, and the root mean square error can be effectively restricted to below 0.4μV, which is less than half the error in traditional methods. After expanding the BCI Competition III 3a dataset from 18 channels to 60 channels, we conducted classification experiments on four types of motor imagery tasks. Compared to the original low-density EEG signals (18 channels), the classification accuracy was around 82%, an increase of about 20%. When juxtaposed with real high-density signals, the increment in the error rate remained below 5%. The expansion of the EEG channels showed a substantial and notable improvement compared with the original low-density signals. Full article

(This article belongs to the Special Issue Combining Brain-Computer Interfaces and Assistive Biosensing Technologies)

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Other

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30 pages, 2046 KB

Open AccessSystematic Review

Non-Invasive BCI-VR Applied Protocols as Intervention Paradigms on School-Aged Subjects with ASD: A Systematic Review

by Archondoula Alexopoulou, Pantelis Pergantis, Constantinos Koutsojannis, Vassilios Triantafillou and Athanasios Drigas

Sensors 2025, 25(5), 1342; https://doi.org/10.3390/s25051342 - 22 Feb 2025

Cited by 3 | Viewed by 9084

Abstract

This paper aims to highlight non-invasive BCI-VR applied protocols as intervention paradigms on school-aged subjects with ASD. Computer-based interventions are considered appropriate for users with ASD as concentration on a screen reduces other stimuli from the environment that are likely to be distracting [...] Read more.

This paper aims to highlight non-invasive BCI-VR applied protocols as intervention paradigms on school-aged subjects with ASD. Computer-based interventions are considered appropriate for users with ASD as concentration on a screen reduces other stimuli from the environment that are likely to be distracting or disruptive. Since there are no social conditions for engagement in such processes and the responses of computing systems do not hold surprises for users, as the outputs are fully controlled, they are ideal for ASD subjects. Children and adolescents with ASD, when supported by BCI interventions through virtual reality applications, especially appear to show significant improvements in core symptoms, such as cognitive and social deficits, regardless of their age or IQ. We examined nine protocols applied from 2016 to 2023, focusing on the BCI paradigms, the procedure, and the outcomes. Our study is non-exhaustive but representative of the state of the art in the field. As concluded by the research, BCI-VR applied protocols have no side effects and are rather easy to handle and maintain, and despite the fact that there are research limitations, they hold promise as a tool for improving social and cognitive skills in school-aged individuals with ASD. Full article

(This article belongs to the Special Issue Combining Brain-Computer Interfaces and Assistive Biosensing Technologies)

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