In this study, 3D brain-computer interface (BCI) training platforms were used to stimulate the subjects for visual motion imagery and visual perception. We measured the activation brain region and alpha-band power activity when the subjects perceived and imagined the stimuli. Based on this, 4-class were classified in visual stimuli session and visual motion imagery session respectively. The results showed that the occipital region is involved in visual perception and visual motion imagery, and alpha-band power is increased in visual motion imagery session and decreased in visual motion stimuli session. Compared with the performance of visual motion imagery and motor imagery, visual motion imagery has higher performance than motor imagery. The binary class was classified using one versus rest approach as well as analysis of brain activation to prove that visual-related brain wave signals are meaningful, and the results were significant.
Designing a feedback that helps participants to achieve higher performances is an important concern in brain-computer interface (BCI) research. In a pilot study, we demonstrate how a congruent auditory feedback could improve classification in a electroencephalography (EEG) motor imagery BCI. This is a promising result for creating alternate feedback modality.
Brain computer interfaces (BCI) decode the electrophysiological signals from the brain into an action that is carried out by a computer or robotic device. Motor imagery BCIs (MI BCI) rely on the user s imagination of bodily movements, however not all users can generate the brain activity needed to control MI BCI. This difference in MI BCI performance among novice users could be due to their cognitive abilities. In this study, the impact of spatial abilities and visuospatial memory on MI BCI performance is investigated. Fifty four novice users participated in a MI BCI task and two cognitive tes…
Mental Imagery based Brain-Computer Interfaces (MI-BCI) are a mean to control digital technologies by performing MI tasks alone. Throughout MI-BCI use, human supervision (e.g., experimenter or caregiver) plays a central role. While providing emotional and social feedback, people present BCIs to users and ensure smooth users' progress with BCI use. Though, very little is known about the influence experimenters might have on the results obtained. Such influence is to be expected as social and emotional feedback were shown to influence MI-BCI performances. Furthermore, literature from different f…
The study reports the performance of Parkinson's disease (PD) patients to operate Motor-Imagery based Brain-Computer Interface (MI-BCI) and compares three selected pre-processing and classification approaches. The experiment was conducted on 7 PD patients who performed a total of 14 MI-BCI sessions targeting lower extremities. EEG was recorded during the initial calibration phase of each session, and the specific BCI models were produced by using Spectrally weighted Common Spatial Patterns (SpecCSP), Source Power Comodulation (SPoC) and Filter-Bank Common Spatial Patterns (FBCSP) methods. The …
Recently, smart home systems based on brain-computer interface (BCI) has attracted a wide range of interests in both industry and academia. However, the current BCI system has several shortcomings as it produces a comparatively lower accuracy for real-time implementations as well as the intuitive paradigm for the users cannot be well established here. Therefore, in this study, we proposed a highly intuitive BCI paradigm that combines event-related potential (ERP) with the speech-imagery task for the individual target objects. The decoding accuracy of the proposed paradigm was 88.1% (plus or mi…
Mental Imagery based Brain-Computer Interfaces (MI-BCI) enable their users to control an interface, e.g., a prosthesis, by performing mental imagery tasks only, such as imagining a right arm movement while their brain activity is measured and processed by the system. Designing and using a BCI requires users to learn how to produce different and stable patterns of brain activity for each of the mental imagery tasks. However, current training protocols do not enable every user to acquire the skills required to use BCIs. These training protocols are most likely one of the main reasons why BCIs re…
With increasing global age and disability assistive robots are becoming more necessary, and brain computer interfaces (BCI) are often proposed as a solution to understanding the intent of a disabled person that needs assistance. Most frameworks for electroencephalography (EEG)-based motor imagery (MI) BCI control rely on the direct control of the robot in Cartesian space. However, for 3-dimensional movement, this requires 6 motor imagery classes, which is a difficult distinction even for more experienced BCI users. In this paper, we present a simulated training and testing framework which redu…
Objective. Neural self-regulation is necessary for achieving control over brain-computer interfaces (BCIs). This can be an arduous learning process especially for motor imagery BCI. Various training methods were proposed to assist users in accomplishing BCI control and increase performance. Notably the use of biased feedback, i.e. non-realistic representation of performance. Benefits of biased feedback on performance and learning vary between users (e.g. depending on their initial level of BCI control) and remain speculative. To disentangle the speculations, we investigate what personality typ…
Brain Computer Interfaces (BCI) provide the opportunity to control external devices using the brain ElectroEncephaloGram (EEG) signals. In this paper we propose two software framework in order to control a 5 degree of freedom robotic and prosthetic hand. Results are presented where an Emotiv Cognitive Suite (i.e. the 1st framework) combined with an embedded software system (i.e. an open source Arduino board) is able to control the hand through character input associated with the taught actions of the suite. This system provides evidence of the feasibility of brain signals being a viable approa…
Before the operation of a motor imagery based brain-computer interface (BCI) adopting machine learning techniques, a cumbersome training procedure is unavoidable. The development of a practical BCI posed the challenge of classifying single-trial EEG with a small training set. In this letter, we addressed this problem by employing a series of signal processing and machine learning approaches to alleviate overfitting and obtained test accuracy similar to training accuracy on the datasets from BCI Competition III and our own experiments.
Stroke is the leading cause of serious and long-term disability worldwide. Some studies have shown that motor imagery (MI) based BCI has a positive effect in poststroke rehabilitation. It could help patients promote the reorganization processes in the damaged brain regions. However, offline motor imagery and conventional online motor imagery with feedback (such as rewarding sounds and movements of an avatar) could not reflect the true intention of the patients. In this study, both virtual limbs and functional electrical stimulation (FES) were used as feedback to provide patients a closed-loop …
EEG-based AI-BCI Wheelchair Advancement: Transformer-Based Learning with Motor Imagery for Brain Computer Interface
Department of Computer Science and Engineering, Kathmandu University（加德满都大学计算机科学与工程系）；Department of Electronics and Communication Engineering, Nepal Engineering College, Pokhara University（博卡拉大学尼泊尔工程学院电子与通信工程系）；Department of Applied Sciences and Chemical Engineering, Institute of Engineering, Pulchowk Campus, Tribhuvan University（特里布万大学工程学院普尔乔克校区应用科学与化学工程系）
Background: Common spatial pattern (CSP) has been widely used for feature extraction in the case of motor imagery (MI) electroencephalogram (EEG) recordings and in MI classification of brain-computer interface (BCI) applications. BCI usually requires relatively long EEG data for reliable classifier training. More specifically, before using general spatial patterns for feature extraction, a training dictionary from two different classes is used to construct a compound dictionary matrix, and the representation of the test samples in the filter band is estimated as a linear combination of the col…
This paper reports on a study in which a novel virtual moving sound-based spatial auditory brain-computer interface (BCI) paradigm is developed. Classic auditory BCIs rely on spatially static stimuli, which are often boring and difficult to perceive when subjects have non-uniform spatial hearing perception characteristics. The concept of moving sound proposed and tested in the paper allows for the creation of a P300 oddball paradigm of necessary target and non-target auditory stimuli, which are more interesting and easier to distinguish. We present a report of our study of seven healthy subjec…
This paper presents a study in task-oriented approach to stroke rehabilitation by controlling a haptic device via near-infrared spectroscopy-based brain-computer interface (BCI). The task is to command the haptic device to move in opposing directions of leftward and rightward movement. Our study consists of data acquisition, signal preprocessing, and classification. In data acquisition, we conduct experiments based on two different mental tasks: one on pure motor imagery, and another on combined motor imagery and action observation. The experiments were conducted in both offline and online mod…
Brain computer interfaces (BCIs) offer individuals suffering from major disabilities an alternative method to interact with their environment. Sensorimotor rhythm (SMRs) based BCIs can successfully perform control tasks; however, the traditional SMR paradigms intuitively disconnect the control and real task, making them non-ideal for complex control scenarios. In this study, we design a new, intuitively connected motor imagery (MI) paradigm using hierarchical common spatial patterns (HCSP) and context information to effectively predict intended hand grasps from electroencephalogram (EEG) data.…
An electroencephalogram is an effective approach that provides a bidirectional pathway between the user and computer in a non-invasive way. In this study, we adopted the visual imagery data for controlling the BCI-based robotic arm. Visual imagery increases the power of the alpha frequency range of the visual cortex over time as the user performs the task. We proposed a deep learning architecture to decode the visual imagery data using only two channels and also we investigated the combination of two EEG channels that has significant classification performance. When using the proposed method, …
In this paper we introduce the concept of Brain-Computer Interface (BCI) inhibitor, which is meant to standby the BCI until the user is ready, in order to improve the overall performance and usability of the system. BCI inhibitor can be defined as a system that monitors user's state and inhibits BCI interaction until specific requirements (e.g. brain activity pattern, user attention level) are met. In this pilot study, a hybrid BCI is designed and composed of a classic synchronous BCI system based on motor imagery and a BCI inhibitor. The BCI inhibitor initiates the control period of the BCI w…
Developments in Brain Computer Interfaces (BCIs) are empowering those with severe physical afflictions through their use in assistive systems. Common methods of achieving this is via Motor Imagery (MI), which maps brain signals to code for certain commands. Electroencephalogram (EEG) is preferred for recording brain signal data on account of it being non-invasive. Despite their potential utility, MI-BCI systems are yet confined to research labs. A major cause for this is lack of robustness of such systems. As hypothesized by two teams during Cybathlon 2016, a particular source of the system's …
Motor imagery (MI) is a mental representation of motor behavior that has been widely used as a control method for a brain-computer interface (BCI), allowing communication for the physically impaired. The performance of MI based BCI mainly depends on the subject's ability to self-modulate EEG signals. Proper training can help naive subjects learn to modulate brain activity proficiently. However, training subjects typically involves abstract motor tasks and is time-consuming. To improve the performance of naive subjects during motor imagery, a novel paradigm was presented that would guide naive …
University of Tsukuba（筑波大学）；RIKEN Brain Science Institute（理化学研究所脑科学研究所）
The paper presents results from a project aiming to create horizontally distributed surround sound sources and virtual sound images as auditory BCI (aBCI) stimuli. The purpose is to create evoked brain wave response patterns depending on attended or ignored sound directions. We propose to use a modified version of the vector based amplitude panning (VBAP) approach to achieve the goal. The so created spatial sound stimulus system for the novel oddball aBCI paradigm allows us to create a multi-command experimental environment with very encouraging results reported in this paper. We also present …
In this paper, the deep learning (DL) approach is applied to a joint training scheme for asynchronous motor imagery-based Brain-Computer Interface (BCI). The proposed DL approach is a cascade of one-dimensional convolutional neural networks and fully-connected neural networks (CNN-FC). The focus is mainly on three types of brain responses: non-imagery EEG (\textit{background EEG}), (\textit{pure imagery}) EEG, and EEG during the transitional period between background EEG and pure imagery (\textit{transitional imagery}). The study of transitional imagery signals should provide greater insight i…
This article examined brain signals of people with disabilities using various signal processing methods to achieve the desired accuracy for utilizing brain-computer interfaces (BCI). EEG signals resulted from 5 mental tasks of word association (WORD), Mental subtraction (SUB), spatial navigation (NAV), right-hand motor imagery (HAND), and feet motor imagery (FEET) of 9 people with central nervous system (CNS) tissue damage were used as input. In processing this data, Butterworth band-pass filter (8-30 Hz) was used in the preprocessing, and CSP, TRCSP, FBCSP methods were used in feature extract…
Average Rankings Mask Per-Subject Optimality: A Friedman-Nemenyi Benchmark of EEG Motor-Imagery BCI Decoders
IBM Technology（IBM技术）；IBM France Lab（IBM法国实验室）；Inclusive Brains（包容大脑）；Wavestone；Human Technology Foundation（人类科技基金会）；Computing and Mathematical Sciences Division（计算与数学科学部）；Mohamed bin Zayed University of Artificial Intelligence (MBZUAI)（Mohamed bin Zayed人工智能大学）；Institute for Artificial Intelligence, Biotech Dental Group（人工智能研究所，生物技术牙科集团）
Universidad Publica de Navarra（纳瓦拉公立大学）；Hospital Complex of Navarre (CHN)（纳瓦拉医院综合体）；University of Technology Sydney（悉尼科技大学）；Australian AI Institute（澳大利亚人工智能研究所）
Brain Computer Interface technologies are popular methods of communication between the human brain and external devices. One of the most popular approaches to BCI is Motor Imagery. In BCI applications, the ElectroEncephaloGraphy is a very popular measurement for brain dynamics because of its non-invasive nature. Although there is a high interest in the BCI topic, the performance of existing systems is still far from ideal, due to the difficulty of performing pattern recognition tasks in EEG signals. BCI systems are composed of a wide range of components that perform signal pre-processing, feat…
The electroencephalogram, a type of non-invasive-based brain signal that has a user intention-related feature provides an efficient bidirectional pathway between user and computer. In this work, we proposed a deep learning framework based on corresponding frequency empahsize method to decode the motor imagery (MI) data from 2020 International BCI competition dataset. The MI dataset consists of 3-class, namely 'Cylindrical', 'Spherical', and 'Lumbrical'. We utilized power spectral density as an emphasize method and a convolutional neural network to classify the modified MI data. The results sho…
Neurophysiological studies are typically conducted in laboratories with limited ecological validity, scalability, and generalizability of findings. This is a significant challenge for the development of brain-computer interfaces (BCIs), which ultimately need to function in unsupervised settings on consumer-grade hardware. We introduce MYND: A framework that couples consumer-grade recording hardware with an easy-to-use application for the unsupervised evaluation of BCI control strategies. Subjects are guided through experiment selection, hardware fitting, recording, and data upload in order to …
RatioWaveNet: A Learnable RDWT Front-End for Robust and Interpretable EEG Motor-Imagery Classification
Department of Electrical, Electronics and Informatics Engineering, University of Catania（电子、电子与信息工程系，卡塔尼亚大学）；Department of Engineering, University of Palermo（工程系，巴勒莫大学）
The brain computer interface (BCI) systems are utilized for transferring information among humans and computers by analyzing electroencephalogram (EEG) recordings.The process of mentally previewing a motor movement without generating the corporal output can be described as motor imagery (MI).In this emerging research field, the number of commands is also limited in relation to the number of MI tasks; in the current literature, mostly two or four commands (classes) are studied. As a solution to this problem, it is recommended to use mental tasks as well as MI tasks. Unfortunately, the use of th…
Federated Motor Imagery Classification for Privacy-Preserving Brain-Computer Interfaces
School of Artificial Intelligence and Automation, Huazhong University of Science and Technology（华中科技大学人工智能与自动化学院）；Shenzhen Huazhong University of Science and Technology Research Institute（华中科技大学深圳研究院）；School of Civil and Hydraulic Engineering, Huazhong University of Science and Technology（华中科技大学土木与水利工程学院）
Similar to most of the real world data, the ubiquitous presence of non-stationarities in the EEG signals significantly perturb the feature distribution thus deteriorating the performance of Brain Computer Interface. In this letter, a novel method is proposed based on Joint Approximate Diagonalization (JAD) to optimize stationarity for multiclass motor imagery Brain Computer Interface (BCI) in an information theoretic framework. Specifically, in the proposed method, we estimate the subspace which optimizes the discriminability between the classes and simultaneously preserve stationarity within …
This study provides a comprehensive test of the head-related impulse response (HRIR) to an auditory spatial speller brain-computer interface (BCI) paradigm, including a comparison with a conventional virtual headphone-based spatial auditory modality. Five BCI-naive users participated in an experiment based on five Japanese vowels. The auditory evoked potentials obtained produced encouragingly good and stable P300-responses in online BCI experiments. Our case study indicates that the auditory HRIR spatial sound paradigm reproduced with headphones could be a viable alternative to established mul…
Non-invasive steady-state visual evoked potential (SSVEP) based brain-computer interface (BCI) systems offer high bandwidth compared to other BCI types and require only minimal calibration and training. Virtual reality (VR) has been already validated as effective, safe, affordable and motivating feedback modality for BCI experiments. Augmented reality (AR) enhances the physical world by superimposing informative, context sensitive, computer generated content. In the context of BCI, AR can be used as a friendlier and more intuitive real-world user interface, thereby facilitating a more seamless…
Brain-computer interface (BCI) decodes brain signals to understand user intention and status. Because of its simple and safe data acquisition process, electroencephalogram (EEG) is commonly used in non-invasive BCI. One of EEG paradigms, motor imagery (MI) is commonly used for recovery or rehabilitation of motor functions due to its signal origin. However, the EEG signals are an oscillatory and non-stationary signal that makes it difficult to collect and classify MI accurately. In this study, we proposed a band-power feature refining convolutional neural network (BFR-CNN) which is composed of …
Noninvasive brain-computer interface (BCI) is widely used to recognize users' intentions. Especially, BCI related to tactile and sensation decoding could provide various effects on many industrial fields such as manufacturing advanced touch displays, controlling robotic devices, and more immersive virtual reality or augmented reality. In this paper, we introduce haptic and sensory perception-based BCI systems called neurohaptics. It is a preliminary study for a variety of scenarios using actual touch and touch imagery paradigms. We designed a novel experimental environment and a device that co…
We describe the experimental procedures for a dataset that we have made publicly available at https://doi.org/10.5281/zenodo.2605204 in mat (Mathworks, Natick, USA) and csv formats. This dataset contains electroencephalographic recordings on 21 subjects doing a visual P300 experiment on PC (personal computer) and VR (virtual reality). The visual P300 is an event-related potential elicited by a visual stimulation, peaking 240-600 ms after stimulus onset. The experiment was designed in order to compare the use of a P300-based brain-computer interface on a PC and with a virtual reality headset, c…
