Since the launch of the first consumer grade EEG measuring sensors ‘NeuroSky Mindset’ in 2007, the market has witnessed an introduction of at least one new product every year by competing manufacturers, which include NeuroSky, Emotiv, interaXon and OpenBCI. There are numerous variations in the make and versions, but these products clearly share the key selling points of affordability, portability, and ease of use. These features are patently well placed provided one of the main objectives for their development is to attract a new target group of commercial users. Nevertheless, with several decades of traditional EEG usage in clinical and experimental settings, the shift toward commercial and engineering sides has not been achieved without skepticism. With this in mind, researchers in related fields have been tirelessly working to ensure that these putatively novel features were not introduced at the expense of efficiency and accuracy by conducting validation studies to compare the performance of data derived from consumer grade EEG devices with ones from standard research grade counterparts. In this review, we seek to provide the detail of the products supplied by the major players, summarize studies that evaluate consumer product’s performance against research grade devices, the key areas of applications that these consumer grade devices have been employed in over the past five years or so, and finally give our perspectives on the limitations and what these innovative tools could offer going forward in terms of research and commercial applications.

Consumer Grade EEG Measuring Sensors as Research Tools: A Review

An electroencephalography (EEG)-based brain–computer interface (BCI) is a system that provides a pathway between the brain and external devices by interpreting EEG. EEG-based BCI applications have initially been developed for medical purposes, with the aim of facilitating the return of patients to normal life. In addition to the initial aim, EEG-based BCI applications have also gained increasing significance in the non-medical domain, improving the life of healthy people, for instance, by making it more efficient, collaborative and helping develop themselves. The objective of this review is to give a systematic overview of the literature on EEG-based BCI applications from the period of 2009 until 2019. The systematic literature review has been prepared based on three databases PubMed, Web of Science and Scopus. This review was conducted following the PRISMA model. In this review, 202 publications were selected based on specific eligibility criteria. The distribution of the research between the medical and non-medical domain has been analyzed and further categorized into fields of research within the reviewed domains. In this review, the equipment used for gathering EEG data and signal processing methods have also been reviewed. Additionally, current challenges in the field and possibilities for the future have been analyzed.

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Past, Present, and Future of EEG-Based BCI Applications

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.

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

Over the last decade, the area of electroencephalography (EEG) witnessed a progressive move from high-end large measurement devices, relying on accurate construction and providing high sensitivity, to miniature hardware, more specifically wireless wearable EEG devices. While accurate, traditional EEG systems need a complex structure and long periods of application time, unwittingly causing discomfort and distress on the users. Given their size and price, aside from their lower sensitivity and narrower spectrum band(s), wearable EEG devices may be used regularly by individuals for continuous collection of user data from non-medical environments. This allows their usage for diverse, nontraditional, non-medical applications, including cognition, BCI, education, and gaming. Given the reduced need for standardization or accuracy, the area remains a rather incipient one, mostly driven by the emergence of new devices that represent the critical link of the innovation chain. In this context, the aim of this study is to provide a holistic assessment of the consumer-grade EEG devices for cognition, BCI, education, and gaming, based on the existing products, the success of their underlying technologies, as benchmarked by the undertaken studies, and their integration with current applications across the four areas. Beyond establishing a reference point, this review also provides the critical and necessary systematic guidance for non-medical EEG research and development efforts at the start of their investigation.

https://www.mdpi.com/2079-9292/9/12/2092/pdf

Wireless Sensors for Brain Activity—A Survey

Wheelchairs controlled using electroencephalography (EEG) have been proposed to facilitate independent mobility for people with motor disabilities. To date, the majority of these systems have relied on distracting external stimuli such as flashing lights and expensive, medical-grade EEG amplifiers. We propose a wheelchair prototype that uses hand motor imagery (MI) and jaw clench data collected with a consumer-grade EEG system to generate left, right, forward, and stop commands. The signal is classified with logistic regression, and using only two scalp electrodes and a two-second window size, we obtained a mean subject accuracy of 60 ± 5% and a peak subject accuracy of 82± 3%. We introduce a novel control-flow paradigm relying on an intermediate control state, engaged by jaw clenching, to reduce the complexity of our classification problem, as well as real-time spectrograms for neurofeedback training. Additionally, we supplement our system with automated driving features, a location tracker, and a heart-rate monitor to increase usability and safety.

A Low-Cost, Semi-Autonomous Wheelchair Controlled by Motor Imagery and Jaw Muscle Activation

Brain-computer interface (BCI) based systems can be used to control external devices by translating a certain set of patterns in the brain signals into actions. An example of this is a BCI controlled wheelchair, with the actions being navigating the wheelchair. In this paper, a Steady State Visual Evoked Potential (SSVEP) based BCI system that controls the operations (forward, reverse, left, and right) of an electrical wheelchair is presented. This system utilizes four LEDs with different flickering frequencies as a visual stimulation source. By providing concentration to these visual stimulation sources, the SSVEP signals are entrained in the occipital and posterior region of the brain. The acquired SSVEP signals are classified into four different frequency ranges representing each of the directions using suitable signal processing algorithms. Based on this classification, a specific control signal is derived to navigate the wheelchair in the desired direction. This system can assist individuals who suffer from neuromuscular degenerative diseases such as Amyotrophic Lateral Sclerosis (ALS), Locked-in Syndrome (LIS), etc., with the navigation of an electric wheelchair using their brain waves. Five peripheral safety sensors are mounted on the wheelchair to avoid the risk of collisions while operating the wheelchair. Once an obstacle is detected by these sensors, a control signal is sent from the Arduino microcontroller to stop the wheelchair. Three experiments were conducted on four subjects to measure the accuracy and reliability of the system. Our results show that the presented system allows the user to navigate to their intended direction with an average accuracy of 79.4%. Also, the safety mechanism incorporated results in an accuracy of 100% obstacle avoidance. Our results show that on average a user can navigate 50 feet set path in approximately five minutes.

An SSVEP based brain computer interface system to control electric wheelchairs

A Brain Computer Interface (BCI) based assistive technology device offers great reliability and access to individuals who are affected by neurodegenerative motor disabilities. As the disease progresses, individuals lose their physical and voluntary movements and only their brain signals remain active. A BCI system using an individual's electroencephalograph (EEG) signal provides a mechanism for these individuals to communicate with the external world. As most of the communication depends on the use of smartphones or tablets, an EEG based iOS communication device is presented in this study. A tactile feedback mechanism, which evokes P300 event related response is used to obtain EEG signals. This tactile or haptic feedback system comprises of three vibrational coin motors attached at the fingertips of the user. When the user concentrates on these motor vibrations, a P300 response is generated. Three P300 responses can be obtained for each of the three motors as these motors run at different revolutions per minute (rpm). The signals obtained are preprocessed, filtered, feature extracted, and given as the control signals to an Arduino. These signals are mapped to an iPhone device by enabling the Bluetooth and Switch control feature for performing various actions such as “move to previous item”, “move to next item”, and “tap”. Three experiments involving four participants were conducted to test the accuracy and reliability of the system. Our results show that a user can correctly navigate to their desired location 66.67% of the time. The system presented in this study can also be used to navigate from an iPhone's home screen to Google in approximately six minutes. The results obtained are promising for individuals who are completely paralyzed. Lastly, the proposed system can also be used to navigate third party applications, such as Sounding Board which is a commonly used assistive technology application that allows the users to select a phrase that will be heard through the iPhone's speaker.

Krupal Sureshbai Mistry

Papers

An SSVEP based brain computer interface system to control electric wheelchairs

A tactile P300 based brain computer interface system for communication in iOS devices