Brain-computer Interface Headset Research Articles

Application of hybrid SSVEP + P300 brain computer interface to control avatar movement in mobile virtual reality gaming environment

IntroductionThis research evaluated the feasibility of a hybrid SSVEP + P300 brain computer interface (BCI) for controlling the movement of an avatar in a virtual reality (VR) gaming environment (VR + BCI). Existing VR + BCI gaming environments have limitations, such as visual fatigue, a lower communication rate, minimum accuracy, and poor system comfort. Hence, there is a need for an optimized hybrid BCI system that can simultaneously evoke the strongest P300 and SSVEP potentials in the cortex. MethodsA BCI headset was coupled with a VR headset to generate a VR + BCI environment. The author developed a VR game in which the avatar’s movement is controlled using the user's cortical responses with the help of a BCI headset. Specifically designed visual stimuli were used in the proposed system to elicit the strongest possible responses from the user's brain. The proposed system also includes an auditory feedback mechanism to facilitate precise avatar movement. Results and conclusionsConventional P300 BCI and SSVEP BCI were also used to control the movements of the avatar, and their performance metrics were compared to those of the proposed system. The results demonstrated that the hybrid SSVEP + P300 BCI system was superior to the other systems for controlling avatar movement.

A Hardware-Based Configurable Algorithm for Eye Blink Signal Detection Using a Single-Channel BCI Headset.

Eye blink artifacts in electroencephalographic (EEG) signals have been used in multiple applications as an effective method for human-computer interaction. Hence, an effective and low-cost blinking detection method would be an invaluable aid for the development of this technology. A configurable hardware algorithm, described using hardware description language, for eye blink detection based on EEG signals from a one-channel brain-computer interface (BCI) headset was developed and implemented, showing better performance in terms of effectiveness and detection time than manufacturer-provided software.

Comparison of Smoothing Filters' Influence on Quality of Data Recorded with the Emotiv EPOC Flex Brain-Computer Interface Headset during Audio Stimulation.

Off-the-shelf, consumer-grade EEG equipment is nowadays becoming the first-choice equipment for many scientists when it comes to recording brain waves for research purposes. On one hand, this is perfectly understandable due to its availability and relatively low cost (especially in comparison to some clinical-level EEG devices), but, on the other hand, quality of the recorded signals is gradually increasing and reaching levels that were offered just a few years ago by much more expensive devices used in medicine for diagnostic purposes. In many cases, a well-designed filter and/or a well-thought signal acquisition method improve the signal quality to the level that it becomes good enough to become subject of further analysis allowing to formulate some valid scientific theories and draw far-fetched conclusions related to human brain operation. In this paper, we propose a smoothing filter based upon the Savitzky–Golay filter for the purpose of EEG signal filtering. Additionally, we provide a summary and comparison of the applied filter to some other approaches to EEG data filtering. All the analyzed signals were acquired from subjects performing visually involving high-concentration tasks with audio stimuli using Emotiv EPOC Flex equipment.

RAPID VISUAL PRESENTATION TO SUPPORT GEOSPATIAL BIG DATA PROCESSING

Abstract. Given the limited number of human GIS/image analysts at any organization, use of their time and organizational resources is important, especially in light of Big Data application scenarios when organizations may be overwhelmed with vast amounts of geospatial data. The current manuscript is devoted to the description of experimental research outlining the concept of Human-Computer Symbiosis where computers perform tasks, such as classification on a large image dataset, and, in sequence, humans perform analysis with Brain-Computer Interfaces (BCIs) to classify those images that machine learning had difficulty with. The addition of the BCI analysis is to utilize the brain’s ability to better answer questions like: “Is the object in this image the object being sought?” In order to determine feasibility of such a system, a supervised multi-layer convolutional neural network (CNN) was trained to detect the difference between ‘ships’ and ‘no ships’ from satellite imagery data. A prediction layer was then added to the trained model to output the probability that a given image was within each of those two classifications. If the probabilities were within one standard deviation of the mean of a gaussian distribution centered at 0.5, they would be stored in a separate dataset for Rapid Serial Visual Presentations (RSVP), implemented with PsyhoPy, to a human analyst using a low cost EMOTIV “Insight” EEG BCI headset. During the RSVP phase, hundreds of images per minute can be sequentially demonstrated. At such a pace, human analysts are not capable of making any conscious decisions about what is in each image; however, the subliminal “aha-moment” still can be detected by the headset. The discovery of these moments are parsed out by exposition of Event Related Potentials (ERPs), specifically the P300 ERPs. If a P300 ERP is generated for detection of a ship, then the relevant image would be moved to its rightful designation dataset; otherwise, if the image classification is still unclear, it is set aside for another RSVP iteration where the time afforded to the analyst for observation of each image is increased each time. If classification is still uncertain after a respectable amount of RSVP iterations, the images in question would be located within the grid matrix of its larger image scene. The adjacent images to those of interest on the grid would then be added to the presentation to give an analyst more contextual information via the expanded field of view. If classification is still uncertain, one final expansion of the field of view is afforded. Lastly, if somehow the classification of the image is indeterminable, the image is stored in an archive dataset.

BCI controlled robotic arm as assistance to the rehabilitation of neurologically disabled patients

Purpose Brain–computer interface (BCI)-controlled assistive robotic systems have been developed with increasing success with the aim to rehabilitation of patients after brain injury to increase independence and quality of life. While such systems may use surgically implanted invasive sensors, non-invasive alternatives can be better suited due to the ease of use, reduced cost, improvements in accuracy and reliability with the advancement of the technology and practicality of use. The consumer-grade BCI devices are often capable of integrating multiple types of signals, including Electroencephalogram (EEG) and Electromyogram (EMG) signals. Materials and Methods This paper summarizes the development of a portable and cost-efficient BCI-controlled assistive technology using a non-invasive BCI headset “OpenBCI” and an open source robotic arm, U-Arm, to accomplish tasks related to rehabilitation, such as access to resources, adaptability or home use. The resulting system used a combination of EEG and EMG sensor readings to control the arm. To avoid risks of injury while the device is being used in clinical settings, appropriate measures were incorporated into the software control of the arm. A short survey was used following the system usability scale (SUS), to measure the usability of the technology to be trialed in clinical settings. Results From the experimental results, it was found that EMG is a very reliable method for assistive technology control, provided that the user specific EMG calibration is done. With the EEG, even though the results were promising, due to insufficient detection of the signal, the controller was not adequate to be used within a neurorehabilitation environment. The survey indicated that the usability of the system is not a barrier for moving the system into clinical trials. Implication on rehabilitation For the rehabilitation of patients suffering from neurological disabilities (particularly those suffering from varying degrees of paralysis), it is necessary to develop technology that bypasses the limitations of their condition. For example, if a patient is unable to walk due to the unresponsiveness in their motor neurons, technology can be developed that used an alternate input to move an exoskeleton, which enables the patient to walk again with the assistance of the exoskeleton. This research focuses on neuro-rehabilitation within the framework of the NHS at the Kent and Canterbury Hospital in UK. The hospital currently does not have any system in place for self-driven rehabilitation and instead relies on traditional rehabilitation methods through assistance from physicians and exercise regimens to maintain muscle movement. This paper summarises the development of a portable and cost-efficient BCI controlled assistive technology using a non-invasive BCI headset “OpenBCI” and an open source robotic arm, U-Arm, to accomplish tasks related to rehabilitation, such as access to resources, adaptability or home use. The resulting system used a combination of EEG and EMG sensor readings to control the arm, which could perform a number of different tasks such as picking/placing objects or assist users in eating.

Towards the Use of Brain–Computer Interface and Gestural Technologies as a Potential Alternative to PIN Authentication

ABSTRACTThis article describes an exploratory study which examined the use of Brain–Computer Interface (BCI) and gestural technologies generated from a BCI headset as a novel potential alternative to a 4-digit PIN code for authentication. Unlike other input modalities, many of these tokens (i.e., “push,” “lift,” “excitement”), can overcome some of the security vulnerabilities associated with PIN authentication (e.g., observations from third parties). Participants engaged in a controlled study where they performed five, 4-token authentication tasks on a simulated authentication screen. The percentage of completed BCI and gestural input tasks, as well as input time and accuracy, was compared to the 4-digit PIN task. The results showed that while authentication using a BCI headset is currently not as complete, fast, or accurate as that of a 4-digit PIN code, users felt that such a system would represent greater security over PIN-based authentication. In addition, mental commands, which might be perceived as the most secure from the standpoint of inconspicuous detection, were found to offer disappointing results both in terms of completion percentage and completion time.

How Should the State of the Brain Be Described?: A call to standardize descriptions of brain states for data collection and research

The IEEE Brain Initiative and the Consumer Technology Association (CTA, formerly the Consumer Electronics Association) promote brain research, development, and commercialization. CTA's recent standardization of electroencephalogram (EEG) signal data interoperability facilitates brain technology developments in the Internet environment for consumer-friendly applications. Emerging brain-computer interface (BCI) consumer products have accelerated new tool development to be blended seamlessly in rehabilitation, education, and entertainment. It has been observed that, while some of these developments flourish via collaboration, others tend to proliferate. To continue the recent commercialization advancement in BCI headsets, it behooves the industry to standardize EEG brain signal collection to form an open databank to allow data exchange via the Internet. Specific issues are raised with respect to the fundamental harmonization of brain states and its linkage to EEG signals for big data analytics and Internet of Things connectivity. Replacing proprietary formats with standard EEG data is the purpose of this article, and the progress and challenges of the ongoing standardization effort within the CTA are discussed.

Are low cost Brain Computer Interface headsets ready for motor imagery applications?

Low cost electroencephalography (EEG) headset devices for brain data capturing are fast becoming a key instrument on Brain Computer Interface (BCI) applications. In spite of being a controller device initially developed for gaming, the research community has adopted them as a key element to gather EEG data. However, there have been little discussion about their performance when being compared with professional and research EEG headsets.This paper provides an assessment of one of these devices, the Emotiv EPOC, on a motor imagery problem. As a benchmark, the data and results presented for the Data Set V of the BCI Competition III have been used, which were recorded with a professional Biosemi Active 2 EEG headset. From the perspective of a final working application, it is shown that the performance of this headset is comparable to that found in professional devices when using the same number of sensors and sensor positions for a three status motor imagery cognitive process. This finding implies an increase on the number of EEG headsets the researchers and manufacturers of BCI systems applied to motor imagery problems can integrate and a reduction of their cost.As part of this paper the Emotiv EPOC recorded raw and pre-processed datasets are published to allow further improvements and comparisons.

Fragmentary shape recognition: A BCI study

Recently, Brain–Computer Interface (BCI) has emerged as a potential modality that utilizes natural and intuitive human mechanisms of thinking process to enable interactions in CAD interfaces. Before BCI could become a mainstream mode of HCI for CAD interfaces; fundamental studies directed towards understanding how humans mentally represent and process the geometry are needed. The outlined work in this paper presents an objective user study to understand shape recognition process in the humans. Specifically, we focus on the fundamental task of fragmentary shape identification. The problem of fragmentary shape recognition can be defined as follows: given a partial and incomplete minimalistic representation of a given shape, can one recognize the actual complete shape or object? In user studies, each subject was progressively (in stages) shown more informative fragmented images of an object to be recognized. During each stage of the experiment, the brain activity of users in the form of electroencephalogram (EEG) signals was recorded with a BCI headset. The recorded signals are then processed to objectively study the fragmentary shape recognition process. The results of user studies conclusively show that the measured brain activities of subjects can serve as a very accurate proxy to estimate subjects fragmentary shape recognition process.

An approach toward wireless brain-computer interface system using EEG signals: A review

The wireless brain–computer interface (BCI) system can become as powerful aids for people with disability (PWD), especially to help them move independently. The BCI system captures the user’s brain activity and classifies into a signal to which a robot or a computer device can respond. In this article, a review of various electrodes for capturing the electroencephalogram (EEG) signals, key techniques, and their applications of wireless BCI headset along with future development issues proposed by many researchers have been discussed. The central idea is a study on the brain rhythm related to the user’smovements for the control of mobile robots, humanoids, and robotic wheelchair. The BCI system is capable of improving and enriching the lives of PWD and people with neuromuscular disorder and bringing back the quality of free movement for PWD.

Evaluation of an anthropometric shape model of the human scalp

This paper presents the evaluation a 3D shape model of the human head. A statistical shape model of the head is created from a set of 100 MRI scans. The ability of the shape model to predict new head shapes is evaluated by considering the prediction error distributions. The effect of using intuitive anthropometric measurements as parameters is examined and the sensitivity to measurement errors is determined. Using all anthropometric measurements, the average prediction error is 1.60 ± 0.36 mm, which shows the feasibility of the new parameters. The most sensitive measurement is the ear height, the least sensitive is the arc length. Finally, two applications of the anthropometric shape model are considered: the study of the male and female population and the design of a brain-computer interface headset. The results show that an anthropometric shape model can be a valuable tool for both research and design.

Human factors study on the usage of BCI headset for 3D CAD modeling

Since its inception, computer aided 3D modeling has primarily relied on the Windows, Icons, Menus, Pointer (WIMP) interface in which user input is in the form of keystrokes and pointer movements. The brain–computer interface (BCI) is a novel modality that uses the brain signals of a user to enable natural and intuitive interaction with an external device. In this paper we present a human factors study on the use of an Emotiv EEG BCI headset for 3D CAD modeling. The study focuses on substituting the conventional computer mouse- and keyboard-based inputs with inputs from the Emotiv EEG headset. The main steps include (1) training the headset to recognize user-specific EEG/EMG signals and (2) assigning the classified signals to emulate keystrokes which are used to activate/control different commands of a CAD package. To assess the performance of the new system, we compared the time taken by the users to create the 3D CAD models using both the conventional and BCI-based interfaces. In addition, to exhibit the adaptability of the new system, we carried out the study for a set of CAD models of varying complexity.

Augmenting the Instant Messaging Experience Through the Use of Brain–Computer Interface and Gestural Technologies

Interpersonal communication benefits greatly from the emotional information encoded by facial expression, body language, and tone of voice. However, these cues are often lost when sending text-based instant messages. This article describes a novel approach utilizing gestural and brain–computer interface (BCI) input to replace the missing emotional cues, with the aim of augmenting the instant messaging process. A set of exploratory studies were conducted using a commercially available BCI headset. An initial study validated the emotional data automatically captured by the device. Subsequently, an instant messaging application was developed, which detected emotions and facial gestures that are presented to the user's chat partner via progress bars and an avatar. Findings from an evaluation revealed that the novel approach facilitates communication containing a greater percentage of affective terms compared with traditional, text-based instant messaging environments. Strong levels of confidence were expressed when using the system to both convey and infer affective states, contributing to a rich subjective user experience.

Towards Brain-Computer Interface Control of a 6-Degree-of-Freedom Robotic Arm Using Dry EEG Electrodes

Introduction. Development of a robotic arm that can be operated using an exoskeletal position sensing harness as well as a dry electrode brain-computer interface headset. Design priorities comprise an intuitive and immersive user interface, fast and smooth movement, portability, and cost minimization.Materials and Methods. A robotic arm prototype capable of moving along 6 degrees of freedom has been developed, along with an exoskeletal position sensing harness which was used to control it. Commercially available dry electrode BCI headsets were evaluated. A particular headset model has been selected and is currently being integrated into the hybrid system.Results and Discussion. The combined arm-harness system has been successfully tested and met its design targets for speed, smooth movement, and immersive control. Initial tests verify that an operator using the system can perform pick and place tasks following a rather short learning curve. Further evaluation experiments are planned for the integrated BCI-harness hybrid setup.Conclusions. It is possible to design a portable robotic arm interface comparable in size, dexterity, speed, and fluidity to the human arm at relatively low cost. The combined system achieved its design goals for intuitive and immersive robotic control and is currently being further developed into a hybrid BCI system for comparative experiments.

Evaluating the ergonomics of BCI devices for research and experimentation

The use of brain computer interface (BCI) devices in research and applications has exploded in recent years. Applications such as lie detectors that use functional magnetic resonance imaging (fMRI) to video games controlled using electroencephalography (EEG) are currently in use. These developments, coupled with the emergence of inexpensive commercial BCI headsets, such as the Emotiv EPOC (http://emotiv.com/index.php) and the Neurosky MindWave, have also highlighted the need of performing basic ergonomics research since such devices have usability issues, such as comfort during prolonged use, and reduced performance for individuals with common physical attributes, such as long or coarse hair. This paper examines the feasibility of using consumer BCIs in scientific research. In particular, we compare user comfort, experiment preparation time, signal reliability and ease of use in light of individual differences among subjects for two commercially available hardware devices, the Emotiv EPOC and the Neurosky MindWave. Based on these results, we suggest some basic considerations for selecting a commercial BCI for research and experimentation. Statement of Relevance: Despite increased usage, few studies have examined the usability of commercial BCI hardware. This study assesses usability and experimentation factors of two commercial BCI models, for the purpose of creating basic guidelines for increased usability. Finding that more sensors can be less comfortable and accurate than devices with fewer sensors.