Low-Cost Robotic Guide Based on a Motor Imagery Brain-Computer Interface for Arm Assisted Rehabilitation

Diversity and Suitability of the State-of-the-Art Wearable and Wireless EEG Systems Review

Wireless electroencephalography (EEG) systems have been attracting increasing attention in recent times. Both the number of articles discussing wireless EEG and their proportion relative to general EEG publications have increased over years. These trends indicate that wireless EEG systems could be more accessible to researchers and the research community has recognized the potential of wireless EEG systems. To explore the development and diverse applications of wireless EEG systems, this review highlights the trends in wearable and wireless EEG systems over the past decade and compares the specifications and research applications of the major wireless systems marketed by 16 companies. For each product, five parameters (number of channels, sampling rate, cost, battery life, and resolution) were assessed for comparison. Currently, these wearable and portable wireless EEG systems have three main application areas: consumer, clinical, and research. To address this multitude of options, the article also discussed the thought process to find a suitable device that meets personalization and use cases specificities. These investigations suggest that low-price and convenience are key factors for consumer applications, wireless EEG systems with FDA or CE-certification may be more suitable for clinical settings, and devices that provide raw EEG data with high-density channels are important for laboratory research. This article presents an overview of the current state of the wireless EEG systems specifications and possible applications and serves as a guide point as it is expected that more influential and novel research will cyclically promote the development of such EEG systems.

EEG-Based BCIs on Motor Imagery Paradigm Using Wearable Technologies: A Systematic Review

In recent decades, the automatic recognition and interpretation of brain waves acquired by electroencephalographic (EEG) technologies have undergone remarkable growth, leading to a consequent rapid development of brain-computer interfaces (BCIs). EEG-based BCIs are non-invasive systems that allow communication between a human being and an external device interpreting brain activity directly. Thanks to the advances in neurotechnologies, and especially in the field of wearable devices, BCIs are now also employed outside medical and clinical applications. Within this context, this paper proposes a systematic review of EEG-based BCIs, focusing on one of the most promising paradigms based on motor imagery (MI) and limiting the analysis to applications that adopt wearable devices. This review aims to evaluate the maturity levels of these systems, both from the technological and computational points of view. The selection of papers has been performed following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA), leading to 84 publications considered in the last ten years (from 2012 to 2022). Besides technological and computational aspects, this review also aims to systematically list experimental paradigms and available datasets in order to identify benchmarks and guidelines for the development of new applications and computational models.

Cross-Platform Implementation of an SSVEP-Based BCI for the Control of a 6-DOF Robotic Arm

Robotics has been successfully applied in the design of collaborative robots for assistance to people with motor disabilities. However, man-machine interaction is difficult for those who suffer severe motor disabilities. The aim of this study was to test the feasibility of a low-cost robotic arm control system with an EEG-based brain-computer interface (BCI). The BCI system relays on the Steady State Visually Evoked Potentials (SSVEP) paradigm. A cross-platform application was obtained in C++. This C++ platform, together with the open-source software Openvibe was used to control a Stäubli robot arm model TX60. Communication between Openvibe and the robot was carried out through the Virtual Reality Peripheral Network (VRPN) protocol. EEG signals were acquired with the 8-channel Enobio amplifier from Neuroelectrics. For the processing of the EEG signals, Common Spatial Pattern (CSP) filters and a Linear Discriminant Analysis classifier (LDA) were used. Five healthy subjects tried the BCI. This work allowed the communication and integration of a well-known BCI development platform such as Openvibe with the specific control software of a robot arm such as Stäubli TX60 using the VRPN protocol. It can be concluded from this study that it is possible to control the robotic arm with an SSVEP-based BCI with a reduced number of dry electrodes to facilitate the use of the system.

Convergent validity and sex invariant factor structure of the Movement Imagery Questionnaire-3 - Second version (MIQ-3S): Healthy, young adult reference data

Aligned with the approach that established the factor structure of the Movement Imagery Questionnaire-3 (MIQ-3), this study extended the two-factor structure of the Movement Imagery Questionnaire - Revised Second version (MIQ-RS). The extension involves assessment of both internal and external visual imagery abilities along with kinesthetic imagery ability. Participants (N = 396) completed the new Movement Imagery Questionnaire - 3 Second Version (MIQ-3S) along with the Vividness of Movement Imagery Questionnaire-2 (VMIQ-2) which measure the same three imagery abilities. Alpha coefficients and between scale Spearman correlations for internal, external, and kinesthetic abilities indicated items were internally consistent (α > 0.87) and established convergent validity (r > 0.69), respectively. MIQ-3S scale means ranged from 5.56 (SD = 1.10) to 5.98 (SD = 0.84), with no differences by sex. The three scales were not multicolinear as intra-scale correlations ranged from 0.47 to 0.61, supporting the three abilities were related, but separate constructs. A multi-trait multimethod confirmatory factor analysis (MTMM CFA), with sex invariance, was conducted to confirm the 3-factor structure of the MIQ-3S. Results from 396 healthy male (n = 200) and female (n = 196) adult college-aged students (M = 21.91, SD = 2.37) indicated a correlated-traits correlated-uniqueness model provided the best fit to the data (CFI = 0.99; SRMR = 0.05; RMSEA = 0.03), while displaying sex invariance. These findings provide baseline data on college-aged, healthy adult participants providing reference data to those investigating imagery abilities among injured populations and practitioners interested in tracking individuals in rehabilitation.

Noninvasive Electroencephalography Equipment for Assistive, Adaptive, and Rehabilitative Brain-Computer Interfaces: A Systematic Literature Review

Humans interact with computers through various devices. Such interactions may not require any physical movement, thus aiding people with severe motor disabilities in communicating with external devices. The brain-computer interface (BCI) has turned into a field involving new elements for assistive and rehabilitative technologies. This systematic literature review (SLR) aims to help BCI investigator and investors to decide which devices to select or which studies to support based on the current market examination. This examination of noninvasive EEG devices is based on published BCI studies in different research areas. In this SLR, the research area of noninvasive BCIs using electroencephalography (EEG) was analyzed by examining the types of equipment used for assistive, adaptive, and rehabilitative BCIs. For this SLR, candidate studies were selected from the IEEE digital library, PubMed, Scopus, and ScienceDirect. The inclusion criteria (IC) were limited to studies focusing on applications and devices of the BCI technology. The data used herein were selected using IC and exclusion criteria to ensure quality assessment. The selected articles were divided into four main research areas: education, engineering, entertainment, and medicine. Overall, 238 papers were selected based on IC. Moreover, 28 companies were identified that developed wired and wireless equipment as means of BCI assistive technology. The findings of this review indicate that the implications of using BCIs for assistive, adaptive, and rehabilitative technologies are encouraging for people with severe motor disabilities and healthy people. With an increasing number of healthy people using BCIs, other research areas, such as the motivation of players when participating in games or the security of soldiers when observing certain areas, can be studied and collaborated using the BCI technology. However, such BCI systems must be simple (wearable), convenient (sensor fabrics and self-adjusting abilities), and inexpensive.

Evaluating the Effect of Stimuli Color and Frequency on SSVEP

Brain-computer interfaces (BCI) can extract information about the subject's intentions by registering and processing electroencephalographic (EEG) signals to generate actions on physical systems. Steady-state visual-evoked potentials (SSVEP) are produced when the subject stares at flashing visual stimuli. By means of spectral analysis and by measuring the signal-to-noise ratio (SNR) of its harmonic contents, the observed stimulus can be identified. Stimulus color matters, and some authors have proposed red because of its ability to capture attention, while others refuse it because it might induce epileptic seizures. Green has also been proposed and it is claimed that white may generate the best signals. Regarding frequency, middle frequencies are claimed to produce the best SNR, although high frequencies have not been thoroughly studied, and might be advantageous due to the lower spontaneous cerebral activity in this frequency band. Here, we show white, red, and green stimuli, at three frequencies: 5 (low), 12 (middle), and 30 (high) Hz to 42 subjects, and compare them in order to find which one can produce the best SNR. We aim to know if the response to white is as strong as the one to red, and also if the response to high frequency is as strong as the one triggered by lower frequencies. Attention has been measured with the Conner's Continuous Performance Task version 2 (CPT-II) task, in order to search for a potential relationship between attentional capacity and the SNR previously obtained. An analysis of variance (ANOVA) shows the best SNR with the middle frequency, followed by the low, and finally the high one. White gives as good an SNR as red at 12 Hz and so does green at 5 Hz, with no differences at 30 Hz. These results suggest that middle frequencies are preferable and that using the red color can be avoided. Correlation analysis also show a correlation between attention and the SNR at low frequency, so suggesting that for the low frequencies, more attentional capacity leads to better results.

SSVEP-assisted RSVP Brain-Computer Interface paradigm for multi-target classification

Brain-Computer Interface (BCI) is actively involved in optimizing the communication medium between the human brain and external devices.

OBJECTIVE

Rapid serial visual presentation (RSVP) is a robust and highly efficient BCI technique in recognizing target objects but suffers from limited target selections. The BCI systems that combine steady-state visual evoked potential (SSVEP) and RSVP can mitigate this limitation and allow users to operate on multiple targets.

APPROACH

This study proposes a novel SSVEP-assisted RSVP BCI model to improve the performance of classifying the target/non-target objects in a multi-target scenario. In this paradigm, SSVEP stimuli helps in identifying the user's focus location and RSVP stimuli that elicits event-related potentials (ERPs) differentiate target and non-target objects.

MAIN RESULTS

The proposed model achieved an offline accuracy of 81.59% by using 12 electroencephalogram (EEG) channels and an online (real-time) accuracy of 78.10% when only 4 EEG channels are considered. Further, the biomarkers of physiological states are analyzed to assess the cognitive states (mental fatigue and user attention) of the participants based on resting theta and alpha band powers. The results indicate an inverse relationship between the BCI performance and the resting EEG power, validating that the subjects' performance is affected by physiological states for prolonged BCI tasks.

SIGNIFICANCE

Our findings demonstrate that the combination of SSVEP and RSVP stimuli improves the BCI performance and further enhances the possibility of performing multiple user command tasks, which are inevitable in real-world applications. Additionally, the cognitive state biomarkers discussed imply the need for an efficient and attractive experimental paradigm that reduces the physiological state disparities and provide enhanced BCI performance.

A BCI System Based on Motor Imagery for Assisting People with Motor Deficiencies in the Limbs

Motor deficiencies constitute a significant problem affecting millions of people worldwide. Such people suffer from a debility in daily functioning, which may lead to decreased and incoherence in daily routines and deteriorate their quality of life (QoL). Thus, there is an essential need for assistive systems to help those people achieve their daily actions and enhance their overall QoL. This study proposes a novel brain–computer interface (BCI) system for assisting people with limb motor disabilities in performing their daily life activities by using their brain signals to control assistive devices. The extraction of useful features is vital for an efficient BCI system. Therefore, the proposed system consists of a hybrid feature set that feeds into three machine-learning (ML) classifiers to classify motor Imagery (MI) tasks. This hybrid feature selection (FS) system is practical, real-time, and an efficient BCI with low computation cost. We investigate different combinations of channels to select the combination that has the highest impact on performance. The results indicate that the highest achieved accuracies using a support vector machine (SVM) classifier are 93.46% and 86.0% for the BCI competition III–IVa dataset and the autocalibration and recurrent adaptation dataset, respectively. These datasets are used to test the performance of the proposed BCI. Also, we verify the effectiveness of the proposed BCI by comparing its performance with recent studies. We show that the proposed system is accurate and efficient. Future work can apply the proposed system to individuals with limb motor disabilities to assist them and test their capability to improve their QoL. Moreover, the forthcoming work can examine the system’s performance in controlling assistive devices such as wheelchairs or artificial limbs.