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Signal Processing Using Non-invasive Physiological Sensors

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A special issue of Sensors (ISSN 1424-8220). This special issue belongs to the section "Intelligent Sensors".

Deadline for manuscript submissions: closed (31 December 2020) | Viewed by 53616

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Special Issue Editors

Dr. Imran Khan Niazi

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Guest Editor

Centre for Chiropractic Research, New Zealand College of Chiropractic, Auckland, New Zealand
Interests: neurorehabilitation; biomedical signal processing; machine learning; applied artificial intelligence; EEG; EMG
Special Issues, Collections and Topics in MDPI journals

Dr. Noman Naseer

E-Mail Website
Guest Editor

Department of Mechatronics Engineering, Air University, Islamabad 44000, Pakistan
Interests: neurorobotics; brain–computer interface; machine learning; artificial intelligence; feature extraction; classification; fNIRS; EMG; EEG
Special Issues, Collections and Topics in MDPI journals

Dr. Hendrik Santosa

E-Mail Website
Guest Editor

Department of Radiology, University of Pittsburgh, Pittsburgh, PA, USA
Interests: functional near-infrared spectroscopy (fNIRS) data processing; statistical analysis for fNIRS signal; multi-modal neuroimaging; brain-computer interface (BCI) applications
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

We would like to cordially invite you to participate in a Special Issue on “Signal Processing Using Non-Invasive Physiological Sensors”. This Special Issue shall concentrate on two aspects:

Non-invasive biomedical sensors for the monitoring of physiological parameters from the human body. Today, a critical factor in providing a cost-effective healthcare system is improving the quality of life and mobility of patients, which can be achieved by developing non-invasive sensor systems, which can then be deployed in point of care, used at home or integrated into wearable devices for long-term data collection.
Another factor which plays an integral part in a cost-effective healthcare system is the signal processing of the data recorded with non-invasive biomedical sensors. In this Special Issue, we aim to attract researchers who are interested in the application of signal processing methods to different biomedical signals such as an electroencephalogram (EEG), electromyogram (EMG), functional near-infrared Spectroscopy (fNIRS), electrocardiogram (ECG), galvanic skin response, pulse oximetry, photoplethysmogram (PPG), etc. We would encourage new signal processing methods or the use of existing signal processing methods for its novel application in the domain of physiological signals to help healthcare providers to make better decisions.

Original papers that describe new research on any of the themes mentioned above are welcomed. We look forward to your participation in this Special Issue.

Dr. Imran Khan Niazi
Dr. Noman Naseer
Dr. Hendrik Santosa
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Sensors is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

Biosensing
Infrared detectors
Portable technology
Cutaneous temperature
EEG
EMG
ECG
fNIRS
Applied signal processing
Machine learning
Artificial intelligence
Real-world application
Clinical environment
Statistical analysis
Multimodal neuroimaging
Artifact detection and removal
Noise removal
Independent component analysis
Signal filtering techniques

Published Papers (11 papers)

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Research

32 pages, 14694 KiB

Open AccessArticle

Expert Hypertension Detection System Featuring Pulse Plethysmograph Signals and Hybrid Feature Selection and Reduction Scheme

by Muhammad Umar Khan, Sumair Aziz, Tallha Akram, Fatima Amjad, Khushbakht Iqtidar, Yunyoung Nam and Muhammad Attique Khan

Sensors 2021, 21(1), 247; https://0-doi-org.brum.beds.ac.uk/10.3390/s21010247 - 02 Jan 2021

Cited by 27 | Viewed by 4128

Abstract

Hypertension is an antecedent to cardiac disorders. According to the World Health Organization (WHO), the number of people affected with hypertension will reach around 1.56 billion by 2025. Early detection of hypertension is imperative to prevent the complications caused by cardiac abnormalities. Hypertension usually possesses no apparent detectable symptoms; hence, the control rate is significantly low. Computer-aided diagnosis based on machine learning and signal analysis has recently been applied to identify biomarkers for the accurate prediction of hypertension. This research proposes a new expert hypertension detection system (EHDS) from pulse plethysmograph (PuPG) signals for the categorization of normal and hypertension. The PuPG signal data set, including rich information of cardiac activity, was acquired from healthy and hypertensive subjects. The raw PuPG signals were preprocessed through empirical mode decomposition (EMD) by decomposing a signal into its constituent components. A combination of multi-domain features was extracted from the preprocessed PuPG signal. The features exhibiting high discriminative characteristics were selected and reduced through a proposed hybrid feature selection and reduction (HFSR) scheme. Selected features were subjected to various classification methods in a comparative fashion in which the best performance of 99.4% accuracy, 99.6% sensitivity, and 99.2% specificity was achieved through weighted k-nearest neighbor (KNN-W). The performance of the proposed EHDS was thoroughly assessed by tenfold cross-validation. The proposed EHDS achieved better detection performance in comparison to other electrocardiogram (ECG) and photoplethysmograph (PPG)-based methods. Full article

(This article belongs to the Special Issue Signal Processing Using Non-invasive Physiological Sensors)

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11 pages, 1601 KiB

Open AccessArticle

Estimation of the Respiratory Rate from Localised ECG at Different Auscultation Sites

by Xinqi Bao, Aimé Kingwengwe Abdala and Ernest Nlandu Kamavuako

Sensors 2021, 21(1), 78; https://0-doi-org.brum.beds.ac.uk/10.3390/s21010078 - 25 Dec 2020

Cited by 10 | Viewed by 5352

Abstract

The respiratory rate (RR) is a vital physiological parameter in prediagnosis and daily monitoring. It can be obtained indirectly from Electrocardiogram (ECG) signals using ECG-derived respiration (EDR) techniques. As part of the study in designing an early cardiac screening system, this work aimed to study whether the accuracy of ECG derived RR depends on the auscultation sites. Experiments were conducted on 12 healthy subjects to obtain simultaneous ECG (at auscultation sites and Lead I as reference) and respiration signals from a microphone close to the nostril. Four EDR algorithms were tested on the data to estimate RR in both the time and frequency domain. Results reveal that: (1) The location of the ECG electrodes between auscultation sites does not impact the estimation of RR, (2) baseline wander and amplitude modulation algorithms outperformed the frequency modulation and band-pass filter algorithms, (3) using frequency domain features to estimate RR can provide more accurate RR except when using the band-pass filter algorithm. These results pave the way for ECG-based RR estimation in miniaturised integrated cardiac screening device. Full article

(This article belongs to the Special Issue Signal Processing Using Non-invasive Physiological Sensors)

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20 pages, 6026 KiB

Open AccessArticle

Enhancing Classification Performance of fNIRS-BCI by Identifying Cortically Active Channels Using the z-Score Method

by Hammad Nazeer, Noman Naseer, Aakif Mehboob, Muhammad Jawad Khan, Rayyan Azam Khan, Umar Shahbaz Khan and Yasar Ayaz

Sensors 2020, 20(23), 6995; https://0-doi-org.brum.beds.ac.uk/10.3390/s20236995 - 07 Dec 2020

Cited by 17 | Viewed by 3776

Abstract

A state-of-the-art brain–computer interface (BCI) system includes brain signal acquisition, noise removal, channel selection, feature extraction, classification, and an application interface. In functional near-infrared spectroscopy-based BCI (fNIRS-BCI) channel selection may enhance classification performance by identifying suitable brain regions that contain brain activity. In this study, the z-score method for channel selection is proposed to improve fNIRS-BCI performance. The proposed method uses cross-correlation to match the similarity between desired and recorded brain activity signals, followed by forming a vector of each channel’s correlation coefficients’ maximum values. After that, the z-score is calculated for each value of that vector. A channel is selected based on a positive z-score value. The proposed method is applied to an open-access dataset containing mental arithmetic (MA) and motor imagery (MI) tasks for twenty-nine subjects. The proposed method is compared with the conventional t-value method and with no channel selected, i.e., using all channels. The z-score method yielded significantly improved (p < 0.0167) classification accuracies of 87.2 ± 7.0%, 88.4 ± 6.2%, and 88.1 ± 6.9% for left motor imagery (LMI) vs. rest, right motor imagery (RMI) vs. rest, and mental arithmetic (MA) vs. rest, respectively. The proposed method is also validated on an open-access database of 17 subjects, containing right-hand finger tapping (RFT), left-hand finger tapping (LFT), and dominant side foot tapping (FT) tasks.The study shows an enhanced performance of the z-score method over the t-value method as an advancement in efforts to improve state-of-the-art fNIRS-BCI systems’ performance. Full article

(This article belongs to the Special Issue Signal Processing Using Non-invasive Physiological Sensors)

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14 pages, 1366 KiB

Open AccessArticle

Decoding Attempted Hand Movements in Stroke Patients Using Surface Electromyography

by Mads Jochumsen, Imran Khan Niazi, Muhammad Zia ur Rehman, Imran Amjad, Muhammad Shafique, Syed Omer Gilani and Asim Waris

Sensors 2020, 20(23), 6763; https://0-doi-org.brum.beds.ac.uk/10.3390/s20236763 - 26 Nov 2020

Cited by 12 | Viewed by 3314

Abstract

Brain- and muscle-triggered exoskeletons have been proposed as a means for motor training after a stroke. With the possibility of performing different movement types with an exoskeleton, it is possible to introduce task variability in training. It is difficult to decode different movement types simultaneously from brain activity, but it may be possible from residual muscle activity that many patients have or quickly regain. This study investigates whether nine different motion classes of the hand and forearm could be decoded from forearm EMG in 15 stroke patients. This study also evaluates the test-retest reliability of a classical, but simple, classifier (linear discriminant analysis) and advanced, but more computationally intensive, classifiers (autoencoders and convolutional neural networks). Moreover, the association between the level of motor impairment and classification accuracy was tested. Three channels of surface EMG were recorded during the following motion classes: Hand Close, Hand Open, Wrist Extension, Wrist Flexion, Supination, Pronation, Lateral Grasp, Pinch Grasp, and Rest. Six repetitions of each motion class were performed on two different days. Hudgins time-domain features were extracted and classified using linear discriminant analysis and autoencoders, and raw EMG was classified with convolutional neural networks. On average, 79 ± 12% and 80 ± 12% (autoencoders) of the movements were correctly classified for days 1 and 2, respectively, with an intraclass correlation coefficient of 0.88. No association was found between the level of motor impairment and classification accuracy (Spearman correlation: 0.24). It was shown that nine motion classes could be decoded from residual EMG, with autoencoders being the best classification approach, and that the results were reliable across days; this may have implications for the development of EMG-controlled exoskeletons for training in the patient’s home. Full article

(This article belongs to the Special Issue Signal Processing Using Non-invasive Physiological Sensors)

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15 pages, 5491 KiB

Open AccessArticle

EMG-Free Monitorization of the Acoustic Startle Reflex with a Mobile Phone: Implications of Sound Parameters with Posture Related Responses

by Christopher L. Gowen, Prashanna Khwaounjoo and Yusuf O. Cakmak

Sensors 2020, 20(21), 5996; https://doi.org/10.3390/s20215996 - 22 Oct 2020

Cited by 1 | Viewed by 3122

Abstract

(1) Background: Acute acoustic (sound) stimulus prompts a state of defensive motivation in which unconscious muscle responses are markedly enhanced in humans. The orbicularis oculi (OO) of the eye is an easily accessed muscle common for acoustic startle reaction/response/reflex (ASR) investigations and is the muscle of interest in this study. Although the ASR can provide insights about numerous clinical conditions, existing methodologies (Electromyogram, EMG) limit the usability of the method in real clinical conditions. (2) Objective: With EMG-free muscle recording in mind, our primary aim was to identify and investigate potential correlations in the responses of individual and cooperative OO muscles to various acoustic stimuli using a mobile and wire-free system. Our secondary aim was to investigate potential altered responses to high and also relatively low intensity acoustics at different frequencies in both sitting and standing positions through the use of biaural sound induction and video diagnostic techniques and software. (3) Methods: This study used a mobile-phone acoustic startle response monitoring system application to collect blink amplitude and velocity data on healthy males, aged 18–28 community cohorts during (n = 30) in both sitting and standing postures. The iPhone X application delivers specific sound parameters and detects blinking responses to acoustic stimulus (in millisecond resolution) to study the responses of the blinking reflex to acoustic sounds in standing and sitting positions by using multiple acoustic test sets of different frequencies and amplitudes introduced as acute sound stimuli (<0.5 s). The single acoustic battery of 15 pure-square wave sounds consisted of frequencies and amplitudes between 500, 1000, 2000, 3000, and 4000 Hz scales using 65, 90, and 105 dB (e.g., 3000 Hz_90 dB). (4) Results: Results show that there was a synchronization of amplitude and velocity between both eyes to all acoustic startles. Significant differences (p = 0.01) in blinking reaction time between sitting vs. standing at the high intensity (105 dB) 500 Hz acoustic test set was discovered. Interestingly, a highly significant difference (p < 0.001) in response times between test sets 500 Hz_105 dB and 4000 Hz_105 dB was identified. (5) Conclusions: To our knowledge, this is the first mobile phone-based acoustic battery used to detect and report significant ASR responses to specific frequencies and amplitudes of sound stimulus with corresponding sitting and standing conditions. The results from this experiment indicate the potential significance of using the specific frequency, amplitude, and postural conditions (as never before identified) which can open new horizons for ASR to be used for diagnosis and monitoring in numerous clinical and remote or isolated conditions. Full article

(This article belongs to the Special Issue Signal Processing Using Non-invasive Physiological Sensors)

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13 pages, 3675 KiB

Open AccessArticle

Eye and Voice-Controlled Human Machine Interface System for Wheelchairs Using Image Gradient Approach

by Saba Anwer, Asim Waris, Hajrah Sultan, Shahid Ikramullah Butt, Muhammad Hamza Zafar, Moaz Sarwar, Imran Khan Niazi, Muhammad Shafique and Amit N. Pujari

Sensors 2020, 20(19), 5510; https://0-doi-org.brum.beds.ac.uk/10.3390/s20195510 - 26 Sep 2020

Cited by 14 | Viewed by 7310

Abstract

Rehabilitative mobility aids are being used extensively for physically impaired people. Efforts are being made to develop human machine interfaces (HMIs), manipulating the biosignals to better control the electromechanical mobility aids, especially the wheelchairs. Creating precise control commands such as move forward, left, right, backward and stop, via biosignals, in an appropriate HMI is the actual challenge, as the people with a high level of disability (quadriplegia and paralysis, etc.) are unable to drive conventional wheelchairs. Therefore, a novel system driven by optical signals addressing the needs of such a physically impaired population is introduced in this paper. The present system is divided into two parts: the first part comprises of detection of eyeball movements together with the processing of the optical signal, and the second part encompasses the mechanical assembly module, i.e., control of the wheelchair through motor driving circuitry. A web camera is used to capture real-time images. The processor used is Raspberry-Pi with Linux operating system. In order to make the system more congenial and reliable, the voice-controlled mode is incorporated in the wheelchair. To appraise the system’s performance, a basic wheelchair skill test (WST) is carried out. Basic skills like movement on plain and rough surfaces in forward, reverse direction and turning capability were analyzed for easier comparison with other existing wheelchair setups on the bases of controlling mechanisms, compatibility, design models, and usability in diverse conditions. System successfully operates with average response time of 3 s for eye and 3.4 s for voice control mode. Full article

(This article belongs to the Special Issue Signal Processing Using Non-invasive Physiological Sensors)

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15 pages, 4996 KiB

Open AccessArticle

A Bipolar-Channel Hybrid Brain-Computer Interface System for Home Automation Control Utilizing Steady-State Visually Evoked Potential and Eye-Blink Signals

by Dalin Yang, Trung-Hau Nguyen and Wan-Young Chung

Sensors 2020, 20(19), 5474; https://0-doi-org.brum.beds.ac.uk/10.3390/s20195474 - 24 Sep 2020

Cited by 21 | Viewed by 3471

Abstract

The goal of this study was to develop and validate a hybrid brain-computer interface (BCI) system for home automation control. Over the past decade, BCIs represent a promising possibility in the field of medical (e.g., neuronal rehabilitation), educational, mind reading, and remote communication. However, BCI is still difficult to use in daily life because of the challenges of the unfriendly head device, lower classification accuracy, high cost, and complex operation. In this study, we propose a hybrid BCI system for home automation control with two brain signals acquiring electrodes and simple tasks, which only requires the subject to focus on the stimulus and eye blink. The stimulus is utilized to select commands by generating steady-state visually evoked potential (SSVEP). The single eye blinks (i.e., confirm the selection) and double eye blinks (i.e., deny and re-selection) are employed to calibrate the SSVEP command. Besides that, the short-time Fourier transform and convolution neural network algorithms are utilized for feature extraction and classification, respectively. The results show that the proposed system could provide 38 control commands with a 2 s time window and a good accuracy (i.e., 96.92%) using one bipolar electroencephalogram (EEG) channel. This work presents a novel BCI approach for the home automation application based on SSVEP and eye blink signals, which could be useful for the disabled. In addition, the provided strategy of this study—a friendly channel configuration (i.e., one bipolar EEG channel), high accuracy, multiple commands, and short response time—might also offer a reference for the other BCI controlled applications. Full article

(This article belongs to the Special Issue Signal Processing Using Non-invasive Physiological Sensors)

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25 pages, 966 KiB

Open AccessArticle

Identification of Motor and Mental Imagery EEG in Two and Multiclass Subject-Dependent Tasks Using Successive Decomposition Index

by Muhammad Tariq Sadiq, Xiaojun Yu, Zhaohui Yuan and Muhammad Zulkifal Aziz

Sensors 2020, 20(18), 5283; https://0-doi-org.brum.beds.ac.uk/10.3390/s20185283 - 16 Sep 2020

Cited by 38 | Viewed by 3526

Abstract

The development of fast and robust brain–computer interface (BCI) systems requires non-complex and efficient computational tools. The modern procedures adopted for this purpose are complex which limits their use in practical applications. In this study, for the first time, and to the best of our knowledge, a successive decomposition index (SDI)-based feature extraction approach is utilized for the classification of motor and mental imagery electroencephalography (EEG) tasks. First of all, the public datasets IVa, IVb, and V from BCI competition III were denoised using multiscale principal analysis (MSPCA), and then a SDI feature was calculated corresponding to each trial of the data. Finally, six benchmark machine learning and neural network classifiers were used to evaluate the performance of the proposed method. All the experiments were performed for motor and mental imagery datasets in binary and multiclass applications using a 10-fold cross-validation method. Furthermore, computerized automatic detection of motor and mental imagery using SDI (CADMMI-SDI) is developed to describe the proposed approach practically. The experimental results suggest that the highest classification accuracy of 97.46% (Dataset IVa), 99.52% (Dataset IVb), and 99.33% (Dataset V) was obtained using feedforward neural network classifier. Moreover, a series of experiments, namely, statistical analysis, channels variation, classifier parameters variation, processed and unprocessed data, and computational complexity, were performed and it was concluded that SDI is robust for noise, and a non-complex and efficient biomarker for the development of fast and accurate motor and mental imagery BCI systems. Full article

(This article belongs to the Special Issue Signal Processing Using Non-invasive Physiological Sensors)

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26 pages, 1182 KiB

Open AccessArticle

CNN and LSTM-Based Emotion Charting Using Physiological Signals

by Muhammad Najam Dar, Muhammad Usman Akram, Sajid Gul Khawaja and Amit N. Pujari

Sensors 2020, 20(16), 4551; https://0-doi-org.brum.beds.ac.uk/10.3390/s20164551 - 14 Aug 2020

Cited by 63 | Viewed by 8099

Abstract

Novel trends in affective computing are based on reliable sources of physiological signals such as Electroencephalogram (EEG), Electrocardiogram (ECG), and Galvanic Skin Response (GSR). The use of these signals provides challenges of performance improvement within a broader set of emotion classes in a less constrained real-world environment. To overcome these challenges, we propose a computational framework of 2D Convolutional Neural Network (CNN) architecture for the arrangement of 14 channels of EEG, and a combination of Long Short-Term Memory (LSTM) and 1D-CNN architecture for ECG and GSR. Our approach is subject-independent and incorporates two publicly available datasets of DREAMER and AMIGOS with low-cost, wearable sensors to extract physiological signals suitable for real-world environments. The results outperform state-of-the-art approaches for classification into four classes, namely High Valence—High Arousal, High Valence—Low Arousal, Low Valence—High Arousal, and Low Valence—Low Arousal. Emotion elicitation average accuracy of

98.73 %

is achieved with ECG right-channel modality, 76.65% with EEG modality, and 63.67% with GSR modality for AMIGOS. The overall highest accuracy of 99.0% for the AMIGOS dataset and 90.8% for the DREAMER dataset is achieved with multi-modal fusion. A strong correlation between spectral- and hidden-layer feature analysis with classification performance suggests the efficacy of the proposed method for significant feature extraction and higher emotion elicitation performance to a broader context for less constrained environments. Full article

(This article belongs to the Special Issue Signal Processing Using Non-invasive Physiological Sensors)

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20 pages, 22193 KiB

Open AccessArticle

Phonocardiogram Signal Processing for Automatic Diagnosis of Congenital Heart Disorders through Fusion of Temporal and Cepstral Features

by Sumair Aziz, Muhammad Umar Khan, Majed Alhaisoni, Tallha Akram and Muhammad Altaf

Sensors 2020, 20(13), 3790; https://0-doi-org.brum.beds.ac.uk/10.3390/s20133790 - 06 Jul 2020

Cited by 57 | Viewed by 6207

Abstract

Congenital heart disease (CHD) is a heart disorder associated with the devastating indications that result in increased mortality, increased morbidity, increased healthcare expenditure, and decreased quality of life. Ventricular Septal Defects (VSDs) and Arterial Septal Defects (ASDs) are the most common types of CHD. CHDs can be controlled before reaching a serious phase with an early diagnosis. The phonocardiogram (PCG) or heart sound auscultation is a simple and non-invasive technique that may reveal obvious variations of different CHDs. Diagnosis based on heart sounds is difficult and requires a high level of medical training and skills due to human hearing limitations and the non-stationary nature of PCGs. An automated computer-aided system may boost the diagnostic objectivity and consistency of PCG signals in the detection of CHDs. The objective of this research was to assess the effects of various pattern recognition modalities for the design of an automated system that effectively differentiates normal, ASD, and VSD categories using short term PCG time series. The proposed model in this study adopts three-stage processing: pre-processing, feature extraction, and classification. Empirical mode decomposition (EMD) was used to denoise the raw PCG signals acquired from subjects. One-dimensional local ternary patterns (1D-LTPs) and Mel-frequency cepstral coefficients (MFCCs) were extracted from the denoised PCG signal for precise representation of data from different classes. In the final stage, the fused feature vector of 1D-LTPs and MFCCs was fed to the support vector machine (SVM) classifier using 10-fold cross-validation. The PCG signals were acquired from the subjects admitted to local hospitals and classified by applying various experiments. The proposed methodology achieves a mean accuracy of 95.24% in classifying ASD, VSD, and normal subjects. The proposed model can be put into practice and serve as a second opinion for cardiologists by providing more objective and faster interpretations of PCG signals. Full article

(This article belongs to the Special Issue Signal Processing Using Non-invasive Physiological Sensors)

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14 pages, 15119 KiB

Open AccessArticle

EEG Headset Evaluation for Detection of Single-Trial Movement Intention for Brain-Computer Interfaces

by Mads Jochumsen, Hendrik Knoche, Troels Wesenberg Kjaer, Birthe Dinesen and Preben Kidmose

Sensors 2020, 20(10), 2804; https://0-doi-org.brum.beds.ac.uk/10.3390/s20102804 - 14 May 2020

Cited by 11 | Viewed by 3633

Abstract

Brain–computer interfaces (BCIs) can be used in neurorehabilitation; however, the literature about transferring the technology to rehabilitation clinics is limited. A key component of a BCI is the headset, for which several options are available. The aim of this study was to test four commercially available headsets’ ability to record and classify movement intentions (movement-related cortical potentials—MRCPs). Twelve healthy participants performed 100 movements, while continuous EEG was recorded from the headsets on two different days to establish the reliability of the measures: classification accuracies of single-trials, number of rejected epochs, and signal-to-noise ratio. MRCPs could be recorded with the headsets covering the motor cortex, and they obtained the best classification accuracies (73%−77%). The reliability was moderate to good for the best headset (a gel-based headset covering the motor cortex). The results demonstrate that, among the evaluated headsets, reliable recordings of MRCPs require channels located close to the motor cortex and potentially a gel-based headset. Full article

(This article belongs to the Special Issue Signal Processing Using Non-invasive Physiological Sensors)

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