Simple Brain-computer Interface Research Articles

On the feasibility of simple brain-computer interface systems for enabling children with severe physical disabilities to explore independent movement.

Children with severe physical disabilities are denied their fundamental right to move, restricting their development, independence, and participation in life. Brain-computer interfaces (BCIs) could enable children with complex physical needs to access power mobility (PM) devices, which could help them move safely and independently. BCIs have been studied for PM control for adults but remain unexamined in children. In this study, we explored the feasibility of BCI-enabled PM control for children with severe physical disabilities, assessing BCI performance, standard PM skills and tolerability of BCI. Patient-oriented pilot trial. Eight children with quadriplegic cerebral palsy attended two sessions where they used a simple, commercial-grade BCI system to activate a PM trainer device. Performance was assessed through controlled activation trials (holding the PM device still or activating it upon verbal and visual cueing), and basic PM skills (driving time, number of activations, stopping) were assessed through distance trials. Setup and calibration times, headset tolerability, workload, and patient/caregiver experience were also evaluated. All participants completed the study with favorable tolerability and no serious adverse events or technological challenges. Average control accuracy was 78.3 ± 12.1%, participants were more reliably able to activate (95.7 ± 11.3%) the device than hold still (62.1 ± 23.7%). Positive trends were observed between performance and prior BCI experience and age. Participants were able to drive the PM device continuously an average of 1.5 meters for 3.0 s. They were able to stop at a target 53.1 ± 23.3% of the time, with significant variability. Participants tolerated the headset well, experienced mild-to-moderate workload and setup/calibration times were found to be practical. Participants were proud of their performance and both participants and families were eager to participate in future power mobility sessions. BCI-enabled PM access appears feasible in disabled children based on evaluations of performance, tolerability, workload, and setup/calibration. Performance was comparable to existing pediatric BCI literature and surpasses established cut-off thresholds (70%) of "effective" BCI use. Participants exhibited PM skills that would categorize them as "emerging operational learners." Continued exploration of BCI-enabled PM for children with severe physical disabilities is justified.

Sensitivity Analysis of a Smart 3D-Printed Hand Prosthetic

In this study, prosthesis performance was examined in the direction of prosthesis comfort, which may be incorporated into clinical practice as considerations for the fabrication of patient-specific prostheses. The need to produce patient-specific prosthetics is very germane to assist in orthopedic and trans-radial amputation medicine. The prosthesis makes use of a relatively simple brain-computer interface that receives electroencephalogram (EEG) signals as input and drives actuators connected to cables to actuate the 3D-printed fingers and the wrist. Both mechanical and electrical simulations were carried out to investigate the response to loading conditions, after which sensitivity analysis was conducted to validate the prosthesis performance.

Unlocking Independence: Exploring Movement with Brain-Computer Interface for Children with Severe Physical Disabilities.

Children with severe physical disabilities are often unable to independently explore their environments, further contributing to complex developmental delays. Brain-computer interfaces (BCIs) could be a novel access method to power mobility for children who struggle to use existing alternate access technologies, allowing them to reap the developmental, social, and psychological benefits of independent mobility. In this pilot study we demonstrated that children with quadriplegic cerebral palsy can use a simple BCI system to explore movement with a power mobility device. Four children were able to use the BCI to drive forward at least 7m, although more practice is needed to achieve more efficient driving skills through sustained BCI activations.

Can Children With Perinatal Stroke Use a Simple Brain Computer Interface?

Perinatal stroke is the leading cause of hemiparetic cerebral palsy resulting in lifelong disability for millions of people worldwide. Options for motor rehabilitation are limited, especially for the most severely affected children. Brain computer interfaces (BCIs) sample brain activity to allow users to control external devices. Functional electrical stimulation enhances motor recovery after stroke, and BCI-activated functional electrical stimulation was recently shown to improve upper extremity function in adult stroke. We aimed to determine the ability of children with perinatal stroke to operate a simple BCI. Twenty-one children with magnetic resonance imaging–confirmed perinatal stroke (57% male, mean [SD] 13.5 [2.6] years, range 9–18) were compared with 24 typically developing controls (71% male, mean age [SD] 13.7 [3.7] years, range 6–18). Participants trained on a simple EEG-based BCI over 2 sessions (10 trials each) utilizing 2 different mental imagery strategies: (1) motor imagery (imagine opening and closing of hands) and (2) goal oriented (imagine effector object moving toward target) to complete 2 tasks: (1) drive a remote controlled car to a target and (2) move a computer cursor to a target. Primary outcome was Cohen Kappa with a score >0.40 suggesting BCI competence. BCI performance was comparable between stroke and control participants. Mean scores were 0.39 (0.18) for stroke versus 0.42 (0.18) for controls (t[42]=0.478, P=0.94). No difference in performance between venous (M=0.45, SD=0.29) and arterial (M=0.34, SD=0.22) stroke (t[82]=1.89, P=0.090) was observed. No effect of task or strategy was observed in the stroke participants. Over 90% of stroke participants demonstrated competency on at least one of the 4 task-strategy combinations. Children with perinatal stroke can achieve proficiency in basic tasks using simple BCI systems. Future directions include exploration of BCI-functional electrical stimulation systems for rehabilitation for children with hemiparesis and other forms of cerebral palsy.

Advancing Brain-Computer Interface Applications for Severely Disabled Children Through a Multidisciplinary National Network: Summary of the Inaugural Pediatric BCI Canada Meeting.

Thousands of youth suffering from acquired brain injury or other early-life neurological disease live, mature, and learn with only limited communication and interaction with their world. Such cognitively capable children are ideal candidates for brain-computer interfaces (BCI). While BCI systems are rapidly evolving, a fundamental gap exists between technological innovators and the patients and families who stand to benefit. Forays into translating BCI systems to children in recent years have revealed that kids can learn to operate simple BCI with proficiency akin to adults. BCI could bring significant boons to the lives of many children with severe physical impairment, supporting their complex physical and social needs. However, children have been neglected in BCI research and a collaborative BCI research community is required to unite and push pediatric BCI development forward. To this end, the pediatric BCI Canada collaborative network (BCI-CAN) was formed, under a unified goal to cooperatively drive forward pediatric BCI innovation and impact. This article reflects on the topics and discussions raised in the foundational BCI-CAN meeting held in Toronto, ON, Canada in November 2019 and suggests the next steps required to see BCI impact the lives of children with severe neurological disease and their families.

A multi-functional BCI system for exigency assistance and environment control based on ML and IoT

Brain-Computer Interface (BCI) is a modality to create an interface which sustains bidirectional communication between the brain and computers. Major disadvantages in implementing such systems are the bulky design and system cost. This study implements a simple multifunction BCI system for the environment control and exigency assistance by just using single channel Electroencephalogram (EEG). In the proposed model, the environment is controlled through Internet of Things (IoT) as per individual's cognitive state while for exigency assistance served as per Event Related Potential (ERP) observed during oddball paradigm. Arduino microcontroller (AMC) hardware is designed for controlling environment. Different Machine Learning (ML) algorithms observed for training the classifiers. Weighted k-Nearest Neighbour (Wk-NN) algorithm trained classifier delivers the best result, with accuracy of 98.3% to detect ERP and 95% accuracy for cognitive state detection. The simple, low cost prototype system was tested for environment control and assistance.

A multi-functional BCI system for exigency assistance and environment control based on ML and IoT

Brain-Computer Interface (BCI) is a modality to create an interface which sustains bidirectional communication between the brain and computers. Major disadvantages in implementing such systems are the bulky design and system cost. This study implements a simple multifunction BCI system for the environment control and exigency assistance by just using single channel Electroencephalogram (EEG). In the proposed model, the environment is controlled through Internet of Things (IoT) as per individual's cognitive state while for exigency assistance served as per Event Related Potential (ERP) observed during oddball paradigm. Arduino microcontroller (AMC) hardware is designed for controlling environment. Different Machine Learning (ML) algorithms observed for training the classifiers. Weighted k-Nearest Neighbour (Wk-NN) algorithm trained classifier delivers the best result, with accuracy of 98.3% to detect ERP and 95% accuracy for cognitive state detection. The simple, low cost prototype system was tested for environment control and assistance.

Peak Detection with Online Electroencephalography (EEG) Artifact Removal for Brain-Computer Interface (BCI) Purposes.

Brain–computer interfaces (BCIs) measure brain activity and translate it to control computer programs or external devices. However, the activity generated by the BCI makes measurements for objective fatigue evaluation very difficult, and the situation is further complicated due to different movement artefacts. The BCI performance could be increased if an online method existed to measure the fatigue objectively and accurately. While BCI-users are moving, a novel automatic online artefact removal technique is used to filter out these movement artefacts. The effects of this filter on BCI performance and mainly on peak frequency detection during BCI use were investigated in this paper. A successful peak alpha frequency measurement can lead to more accurately determining objective user fatigue. Fifteen subjects performed various imaginary and actual movements in separate tasks, while fourteen electroencephalography (EEG) electrodes were used. Afterwards, a steady-state visual evoked potential (SSVEP)-based BCI speller was used, and the users were instructed to perform various movements. An offline curve fitting method was used for alpha peak detection to assess the effect of the artefact filtering. Peak detection was improved by the filter, by finding 10.91% and 9.68% more alpha peaks during simple EEG recordings and BCI use, respectively. As expected, BCI performance deteriorated from movements, and also from artefact removal. Average information transfer rates (ITRs) were 20.27 bit/min, 16.96 bit/min, and 14.14 bit/min for the (1) movement-free, (2) the moving and unfiltered, and (3) the moving and filtered scenarios, respectively.

Evaluating If Children Can Use Simple Brain Computer Interfaces.

Background: The options for severely disabled children with intact cognition to interact with their environment are extremely limited. A brain computer interface (BCI) has the potential to allow such persons to gain meaningful function, communication, and independence. While the pediatric population might benefit most from BCI technology, research to date has been predominantly in adults.Methods: In this prospective, cross-over study, we quantified the ability of healthy school-aged children to perform simple tasks using a basic, commercially available, EEG-based BCI. Typically developing children aged 6–18 years were recruited from the community. BCI training consisted of a brief set-up and EEG recording while performing specific tasks using an inexpensive, commercially available BCI system (EMOTIV EPOC). Two tasks were trained (driving a remote-control car and moving a computer cursor) each using two strategies (sensorimotor and visual imagery). Primary outcome was the kappa coefficient between requested and achieved performance. Effects of task, strategy, age, and learning were also explored.Results: Twenty-six of thirty children completed the study (mean age 13.2 ± 3.6 years, 27% female). Tolerability was excellent with >90% reporting the experience as neutral or pleasant. Older children achieved performance comparable to adult studies, but younger age was associated with lesser though still good performance. The car task demonstrated higher performance compared to the cursor task (p = 0.027). Thought strategy was also associated with performance with visual imagery strategies outperforming sensorimotor approaches (p = 0.031).Conclusion: Children can quickly achieve control and execute multiple tasks using simple EEG-based BCI systems. Performance depends on strategy, task and age. Such success in the developing brain mandates exploration of such practical systems in severely disabled children.

A Single-Stimulus, Multitarget BCI Based on Retinotopic Mapping of Motion-Onset VEPs.

We present a new type of brain-computer interface (BCI) that utilizes the retinotopic mapping of motion-onset visual evoked potentials (mVEP) to accomplish four control channels using a single motion stimulus. Participants selected a BCI command by fixating one of four target locations around a centrally presented visual motion stimulus. A template-matching method was employed to recognize the users' intention by decoding the position of the motion stimulus in the peripheral visual field, and classification performances were evaluated in an offline manner. The motion stimulus eccentricity between the targets and the visual motion stimulus varied among 5.1°, 6.7°, 9.8°, and 13.0°. Distinct N200 spatial patterns were elicited when participants directed attention overtly to the target locations. A four-class classification accuracy of 72.2 ± 5.05% was achieved with a distance of 5.1° visual angle between the targets and the visual motion stimulus. The classification accuracies decreased with increasing motion stimulus eccentricities but remained separable well above the chance level at 13.0° (47.3 ± 8.54%). Our results support the feasibility of a single-stimulus, multitarget mVEP BCI. The proposed system can simplify the visual stimulation of mVEP BCIs, improve user experience and pave the way for simple yet efficient BCI communication.

A Long-Term BCI Study With ECoG Recordings in Freely Moving Rats

Brain Computer Interface (BCI) studies are performed in an increasing number of applications. Questions are raised about electrodes, data processing and effectors. Experiments are needed to solve these issues. To develop a simple BCI set-up to easier studies for improving the mathematical tools to process the ECoG to control an effector. We designed a simple BCI using transcranial electrodes (17 screws, three mechanically linked to create a common reference, 14 used as recording electrodes) to record Electro-Cortico-Graphic (ECoG) neuronal activities in rodents. The data processing is based on an online self-paced non-supervised (asynchronous) BCI paradigm. N-way partial least squares algorithm together with Continuous Wavelet Transformation of ECoG recordings detect signatures related to motor activities. Signature detection in freely moving rats may activate external effectors during a behavioral task, which involved pushing a lever to obtain a reward. After routine training, we showed that peak brain activity preceding a lever push (LP) to obtain food reward was located mostly in the cerebellar cortex with a higher correlation coefficient, suggesting a strong postural component and also in the occipital cerebral cortex. Analysis of brain activities provided a stable signature in the high gamma band (∼180Hz) occurring within 1500 msec before the lever push approximately around -400 msec to -500 msec. Detection of the signature from a single cerebellar cortical electrode triggers the effector with high efficiency (68% Offline and 30% Online) and rare false positives per minute in sessions about 30 minutes and up to one hour (∼2 online and offline). In summary, our results are original as compared to the rest of the literature, which involves rarely rodents, a simple BCI set-up has been developed in rats, the data show for the first time long-term, up to one year, unsupervised online control of an effector.

Comparison of EEG signal decomposition methods in classification of motor-imagery BCI

A brain–computer interface (BCI) provides a link between the human brain and a computer. The task of discriminating four classes (left and right hands and feet) of motor imagery movements of a simple limb-based BCI is still challenging because most imaginary movements in the motor cortex have close spatial representations. We aimed to classify binary limb movements, rather than the direction of movement within one limb. We also investigated joint time-frequency methods to improve classification accuracies. Neither of these, to our knowledge, has been investigated previously in BCI. We recorded EEG data from eleven participants, and demonstrated the classification of four classes of simple-limb motor imagery with an accuracy of 91.46% using intrinsic time-scale decomposition and 88.99% using empirical mode decomposition. In binary classifications, we achieved average accuracies of 89.90% when classifying imaginary movements of left hand versus right hand, 93.1% for left hand versus right foot, 94.00% for left hand versus left foot, 83.82% for left foot versus right foot, 97.62% for right hand versus left foot, and 95.11% for right hand versus right foot. The results show that the binary classification performance is slightly better than that of four-class classification. Our results also show that there is no significant difference in terms of spatial distribution between left and right foot motor imagery movements. There is also no difference in classification performances involving left or right foot movement. This work demonstrates that binary and four-class movements of the left and right feet and hands can be classified using recorded EEG signals of the motor cortex, and an intrinsic time-scale decomposition (ITD) feature extraction method can be used for real time brain computer interface.

Probabilistic co-adaptive brain–computer interfacing

Objective. Brain–computer interfaces (BCIs) are confronted with two fundamental challenges: (a) the uncertainty associated with decoding noisy brain signals, and (b) the need for co-adaptation between the brain and the interface so as to cooperatively achieve a common goal in a task. We seek to mitigate these challenges. Approach. We introduce a new approach to brain–computer interfacing based on partially observable Markov decision processes (POMDPs). POMDPs provide a principled approach to handling uncertainty and achieving co-adaptation in the following manner: (1) Bayesian inference is used to compute posterior probability distributions (‘beliefs’) over brain and environment state, and (2) actions are selected based on entire belief distributions in order to maximize total expected reward; by employing methods from reinforcement learning, the POMDP’s reward function can be updated over time to allow for co-adaptive behaviour. Main results. We illustrate our approach using a simple non-invasive BCI which optimizes the speed–accuracy trade-off for individual subjects based on the signal-to-noise characteristics of their brain signals. We additionally demonstrate that the POMDP BCI can automatically detect changes in the user’s control strategy and can co-adaptively switch control strategies on-the-fly to maximize expected reward. Significance. Our results suggest that the framework of POMDPs offers a promising approach for designing BCIs that can handle uncertainty in neural signals and co-adapt with the user on an ongoing basis. The fact that the POMDP BCI maintains a probability distribution over the user’s brain state allows a much more powerful form of decision making than traditional BCI approaches, which have typically been based on the output of classifiers or regression techniques. Furthermore, the co-adaptation of the system allows the BCI to make online improvements to its behaviour, adjusting itself automatically to the user’s changing circumstances.

Electroencephalography (EEG)-based neurofeedback training for brain–computer interface (BCI)

Electroencephalography has become a popular tool in basic brain research, but in recent years, several practical limitations have been highlighted. Some of the drawbacks pertain to the offline analyses of the neural signal that prevent the subjects from engaging in real-time error correction during learning. Other limitations include the complex nature of the visual stimuli, often inducing fatigue and introducing considerable delays, possibly interfering with spontaneous performance. By replacing the complex external visual input with internally driven motor imagery, we can overcome some delay problems, at the expense of losing the ability to precisely parameterize features of the input stimulus. To address these issues, we here introduce a nontrivial modification to brain-computer Interfaces (BCI). We combine the fast signal processing of motor imagery with the ability to parameterize external visual feedback in the context of a very simple control task: attempting to intentionally control the direction of an external cursor on command. By engaging the subject in motor imagery while providing real-time visual feedback on their instantaneous performance, we can take advantage of positive features present in both externally- and internally driven learning. We further use a classifier that automatically selects the cortical activation features that most likely maximize the performance accuracy. Under this closed loop coadaptation system, we saw a progression of the cortical activation that started in sensorymotor areas, when at chance performance motor imagery was explicitly used, migrated to BA6 under deliberate control and ended in the more frontal regions of prefrontal cortex, when at maximal performance accuracy, the subjects reportedly developed spontaneous mental control of the instructed direction. We discuss our results in light of possible applications of this simple BCI paradigm to study various cognitive phenomena involving the deliberate control of a directional signal in decision making tasks performed with intent.

A low-cost EEG system-based hybrid brain-computer interface for humanoid robot navigation and recognition.

This paper describes a hybrid brain-computer interface (BCI) technique that combines the P300 potential, the steady state visually evoked potential (SSVEP), and event related de-synchronization (ERD) to solve a complicated multi-task problem consisting of humanoid robot navigation and control along with object recognition using a low-cost BCI system. Our approach enables subjects to control the navigation and exploration of a humanoid robot and recognize a desired object among candidates. This study aims to demonstrate the possibility of a hybrid BCI based on a low-cost system for a realistic and complex task. It also shows that the use of a simple image processing technique, combined with BCI, can further aid in making these complex tasks simpler. An experimental scenario is proposed in which a subject remotely controls a humanoid robot in a properly sized maze. The subject sees what the surrogate robot sees through visual feedback and can navigate the surrogate robot. While navigating, the robot encounters objects located in the maze. It then recognizes if the encountered object is of interest to the subject. The subject communicates with the robot through SSVEP and ERD-based BCIs to navigate and explore with the robot, and P300-based BCI to allow the surrogate robot recognize their favorites. Using several evaluation metrics, the performances of five subjects navigating the robot were quite comparable to manual keyboard control. During object recognition mode, favorite objects were successfully selected from two to four choices. Subjects conducted humanoid navigation and recognition tasks as if they embodied the robot. Analysis of the data supports the potential usefulness of the proposed hybrid BCI system for extended applications. This work presents an important implication for the future work that a hybridization of simple BCI protocols provide extended controllability to carry out complicated tasks even with a low-cost system.

An FPGA-Based Brain-Computer Interface for Wireless Electric Wheelchairs

A wireless EEG-based brain-computer interface (BCI) and an FPGA-based system to control electric wheelchairs through a Bluetooth interface was proposed in this paper for paralyzed patients. Paralytic patients can not move freely and only use wheelchairs in their daily life. Especially, people getting motor neuron disease (MND) can only use their eyes and brain to exercise their willpower. Therefore, real-time EEG and winking signals can help these patients effectively. However, current BCI systems are usually complex and have to send the brain waves to a personal computer or a single-chip microcontroller to process the EEG signals. In this paper, a simple BCI system with two channels and an FPGA-based circuit for controlling DC motor can help paralytic patients easily to drive the electric wheelchair. The proposed BCI system consists of a wireless physiological with two-channel acquisition module and an FPGA-based signal processing unit. Here, the physiological signal acquisition module and signal processing unit were designed for extracting EEG and winking signals from brain waves which can directly transformed into control signals to drive the electric wheelchairs. The advantages of the proposed BCI system are low power consumption and compact size so that the system can be suitable for the paralytic patients. The experimental results showed feasible action for the proposed BCI system and drive circuit with a practical operating in electric wheelchair applications.

Implementation of a Simple Brain-Computer Interface Composed of a Compact EEG Recording Device, a Smartphone, and Microcomputers with ZigBee Modules

Implementation of a Simple Brain-Computer Interface Composed of a Compact EEG Recording Device, a Smartphone, and Microcomputers with ZigBee Modules

Virtual reality for assessment of patients suffering chronic pain: a case study

The study of body representation and ownership has been a very active research area in recent years. Synchronous multisensory stimulation has been used for the induction of the illusion of ownership over virtual body parts and even full bodies, and it has provided experimental paradigms for the understanding of the brain processing of body representation. However, the illusion of ownership of a virtual body has rarely been used for patient evaluation and diagnosis. Here we propose a method that exploits ownership of a virtual body in combination with a simple brain computer interface (BCI) and basic physiological measures to complement neurological assessment. A male patient presenting a fixed posture dystonia featuring a permanently closed left fist participated in this case study. The patient saw a virtual body that substituted his own after donning a head-mounted display and thereby entering the virtual reality. The left virtual hand had the same posture as his corresponding real hand. After inducing virtual hand ownership by correlated visuo-tactile stimulation and dynamic reflections in a virtual mirror, the virtual hand would open either automatically or through a cognitive task assessed through a BCI that required him to focus attention on the virtual hand. The results reveal that body ownership induced changes on electromyography and BCI performance in the patient that were different from those in five healthy controls. Overall, the case study shows that the induction of virtual body ownership combined with simple electrophysiological measures could be useful for the diagnosis of patients with neurological conditions.

Using ipsilateral motor signals in the unaffected cerebral hemisphere as a signal platform for brain–computer interfaces in hemiplegic stroke survivors

Brain–computer interface (BCI) systems have emerged as a method to restore function and enhance communication in motor impaired patients. To date, this has been applied primarily to patients who have a compromised motor outflow due to spinal cord dysfunction, but an intact and functioning cerebral cortex. The cortical physiology associated with movement of the contralateral limb has typically been the signal substrate that has been used as a control signal. While this is an ideal control platform in patients with an intact motor cortex, these signals are lost after a hemispheric stroke. Thus, a different control signal is needed that could provide control capability for a patient with a hemiparetic limb. Previous studies have shown that there is a distinct cortical physiology associated with ipsilateral, or same-sided, limb movements. Thus far, it was unknown whether stroke survivors could intentionally and effectively modulate this ipsilateral motor activity from their unaffected hemisphere. Therefore, this study seeks to evaluate whether stroke survivors could effectively utilize ipsilateral motor activity from their unaffected hemisphere to achieve this BCI control. To investigate this possibility, electroencephalographic (EEG) signals were recorded from four chronic hemispheric stroke patients as they performed (or attempted to perform) real and imagined hand tasks using either their affected or unaffected hand. Following performance of the screening task, the ability of patients to utilize a BCI system was investigated during on-line control of a one-dimensional control task. Significant ipsilateral motor signals (associated with movement intentions of the affected hand) in the unaffected hemisphere, which were found to be distinct from rest and contralateral signals, were identified and subsequently used for a simple online BCI control task. We demonstrate here for the first time that EEG signals from the unaffected hemisphere, associated with overt and imagined movements of the affected hand, can enable stroke survivors to control a one-dimensional computer cursor rapidly and accurately. This ipsilateral motor activity enabled users to achieve final target accuracies between 68% and 91% within 15 min. These findings suggest that ipsilateral motor activity from the unaffected hemisphere in stroke survivors could provide a physiological substrate for BCI operation that can be further developed as a long-term assistive device or potentially provide a novel tool for rehabilitation.

Brain–computer interface using a simplified functional near-infrared spectroscopy system

A brain–computer interface (BCI) is a device that allows a user to communicate with external devices through thought processes alone. A novel signal acquisition tool for BCIs is near-infrared spectroscopy (NIRS), an optical technique to measure localized cortical brain activity. The benefits of using this non-invasive modality are safety, portability and accessibility. A number of commercial multi-channel NIRS system are available; however we have developed a straightforward custom-built system to investigate the functionality of a fNIRS-BCI system. This work describes the construction of the device, the principles of operation and the implementation of a fNIRS-BCI application, ‘Mindswitch’ that harnesses motor imagery for control. Analysis is performed online and feedback of performance is presented to the user. Mindswitch presents a basic ‘on/off’ switching option to the user, where selection of either state takes 1 min. Initial results show that fNIRS can support simple BCI functionality and shows much potential. Although performance may be currently inferior to many EEG systems, there is much scope for development particularly with more sophisticated signal processing and classification techniques. We hope that by presenting fNIRS as an accessible and affordable option, a new avenue of exploration will open within the BCI research community and stimulate further research in fNIRS-BCIs.