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Brain-computer interfaces (BCI) register changes in brain activity and utilize this to control computers. The most widely used method is based on registration of electrical signals from the cerebral cortex using extracranially placed electrodes also called electroencephalography (EEG). The features extracted from the EEG may, besides controlling the computer, also be fed back to the patient for instance as visual input. This facilitates a learning process. BCI allow us to utilize brain activity in the rehabilitation of patients after stroke. The activity of the cerebral cortex varies with the type of movement we imagine, and by letting the patient know the type of brain activity best associated with the intended movement the rehabilitation process may be faster and more efficient. The focus of BCI utilization in medicine has changed in recent years. While we previously focused on devices facilitating communication in the rather few patients with locked-in syndrome, much interest is now devoted to the therapeutic use of BCI in rehabilitation. For this latter group of patients, the device is not intended to be a lifelong assistive companion but rather a ‘teacher' during the rehabilitation period.

Brain-computer interfaces (BCI) in the broad sense refer to the ability to use signals measured in the brain to control computers. The field is growing rapidly these years with many new applications and a rapid growth in the number of publications. A recent roadmap of the future of BCI is the result of an international collaboration [1]. The roadmap divides the broad term BCI into pure BCI, in which only the brain signal is interfaced, and the more heterogeneous group of BNCI (brain neuronal computer interaction), in which other measures like muscle signal and gaze direction are taken into account. In this chapter, we will use BCI in the broad sense including knowledge of other modalities than electroencephalography (EEG), i.e. muscle activity, which is important when using BCI in rehabilitation.

There is a number of existing and potential nonmedical uses of BCI like education, gaming, neuroeconomics, safety and security not dealt with here. In the medical field, BCI may be used in mental state monitoring or detection of certain events like craving in addicts, which is also not discussed here. This chapter focuses on the use of BCI in motor-impaired patients.

A typical source of brain signals for BCI is EEG due to high temporal resolution, ease of moving around and relatively low cost. Several groups have worked with other systems like functional magnetic resonance imaging, magnetoencephalography and near-infrared spectroscopy. But most research now and in the nearest future is based on EEG signals.

EEG signals are either recorded from electrodes placed during surgery (invasive) or adhered to the skin (noninvasive) (fig. 1). There are advantages and disadvantages of both approaches. The invasive techniques give a much better signal-to-noise ratio and allow for stable recordings for weeks and months. On the other hand, placing of the electrodes is costly and risky and there is an important long-term risk of infections and bleeding. The surface electrodes, on the other hand, are risk free and easily adhered to the skin but procedures involving hair removal or skin abrasion among other things may take up to 20 min every time the system is set up. Furthermore, the use of extracranial electrodes is associated with a decreased signal-to-noise ratio and there is a risk of slight imperfection of electrode placement which greatly deteriorates the signal.

Fig. 1

Most of the various forms of BCI belong to one of the eight groups on the figure. The area labeled ‘R' refers to typical rehabilitation use. In this field, the recording devices are places on the outer surface of the body and the recorded signal is triggered from an external source rather than being spontaneously generated (top right corner of the figure). Since other body signals play a crucial role, the outermost circle best describes the recorded signals.

Fig. 1

Most of the various forms of BCI belong to one of the eight groups on the figure. The area labeled ‘R' refers to typical rehabilitation use. In this field, the recording devices are places on the outer surface of the body and the recorded signal is triggered from an external source rather than being spontaneously generated (top right corner of the figure). Since other body signals play a crucial role, the outermost circle best describes the recorded signals.

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The recorded EEG shows large variations over time depending on sleep, level of arousal, anxiety and number of artifacts generated within or outside the body. When no specific external stimuli are applied and the subject does not perform specific tasks, we talk about spontaneous EEG. The spontaneous EEG pattern changes to a task-specific pattern when the subject performs a task. Such a task could be imagining a movement or actually performing a movement. Typical patterns in these conditions are event-related synchronization or event-related desynchronization, which appear over the contralateral motor cortex [2]. Such patterns are referred to as endogenous.

EEG patterns may also be secondary to external events. We call those exogenous. The external event can be a simple signal initiating or modifying a specific task or it can be repeated stimulation like flickering symbols. In the first case, the external event helps defining the time at which to look for a physiological signal change. In the case of repeated stimulation, simple often short-lasting EEG changes in response to each stimulus leads to a so-called evoked potential. One way to use these evoked potentials is to compare the timing of objects flickering with different frequency to the recorded potentials.

A large number of medical conditions are associated with disabilities. Some rare conditions like amyotrophic lateral sclerosis and locked-in syndrome are associated with a normal brain unable to take command of any muscles. For these patients, any way to make themselves understood by others is paramount. Many of the first BCI systems were designed with these people in mind. A large number of persons suffer a stroke, traumatic brain injury or cerebral palsy. They may have a range of different problems from language and cognition to motor performance and with varying degrees of disability. Thus, their need for BCI also varies a lot. Now that BCI are becoming cheaper, more flexible and more powerful, many of these persons for whom we previously saw no use of BCI may well benefit from these systems in the future.

In recent years, BCI have moved out of the laboratories and into the hospitals and homes of disabled people. The central task is to improve the lives of the disabled through widely different approaches. One approach is that the BCI is used as a personal assistive technology (PAT) to help the patients with activities of daily living like communicating with people around them or via the Internet, controlling a wheelchair or drawing the curtains. The other approach is therapy based with the goal to train the motor system in disabled patients so that they can eventually give up the BCI and interact with their surroundings naturally. Thus, the goal is not to provide motor control or facilitate communication, but to produce permanent or lasting behavioral changes. Neither the PAT-based nor the therapy-based use of BCI can stand alone and has to be integrated with existing empowerments and therapies.

BCI may be a useful tool for accelerating motor rehabilitation. Motor rehabilitation of patients after for instance stroke often involves motor imagery and assisted movements. If a BCI is included, it may augment the rehabilitation process. A physiotherapist often facilitates the rehabilitation process by passive movements accompanied by asking the patient to imagine the movement. A skilled therapist will be able to evaluate the actual movements and feedback to the patient. However, even the most skilled therapist is unable to monitor the brain activity associated with this process without a BCI. Thus therapist and patients can obtain additional information if a BCI is employed demonstrating which brain activity is associated with a given training activity. Thus, the patient not only (re-)learns to activate the muscles but he also learns what constitutes the optimal brain activity associated with the movement. The motor activity may be directly observed or measured as electrical muscle signals or accelerometer output. Other possible approaches involve BCI directly controlling a training robot [3,4].

An unfortunate real-world limitation in rehabilitation of stroke patients is the amount of resources available for the individual patient. Much research indicates that many patients would benefit from more training than they actually get [5]. When the patient is left to do training on his own, the BCI may play an important role in inducing compensatory plasticity. The data recorded may be turned into time-frequency maps of event-related potentials or focus on specific frequency bands which are considered effective tools to monitor motor imagery and may effectively be combined with actual motor performance [6,7].

Not even the most intense training program combined with optimal brain plasticity and treatment leads to full recovery in all patients. When patients do not recover fully, they may benefit from PAT helping them do things they will never be able to do again in any other way. Thus, the purpose of PAT is not training the patient but assisting him with daily activities at whatever level he is currently functioning.

In a fruitful ongoing collaboration between the University of Copenhagen, Rigshospitalet and the Technical University of Denmark, a number of BCI applications have been developed. Many of the applications may serve as useful assistive devices for disabled persons or serve as a basis for BCI-based therapy. Table 1 summarizes some of the results we have obtained.

Table 1

BCI collaborative results

BCI collaborative results
BCI collaborative results

The use of endogenous signals to control a spelling device was inspired by Hex-o-Spell from the Berlin group [8]. The original approach was based on a 2-class movement of the hands. The left hand signal turned a dial on a hexagon with groups of letters in the corners. The right hand signal lengthened the dial until a group of letters was selected. Then a new hexagon appeared with individual letters from the selected group placed at each corner, and a selection was performed in a similar way. The speed was 6-8 letters per minute corresponding to a little more than 1 word per minute and accuracy was acceptable. We improved this method in several ways. Organizing the letters so that the more common were reached first could increase the speed, but required more learning since the order was no longer alphabetical. By adding a 3rd condition, i.e. movement of the right foot, we were able to switch between the spelling hexagon and another hexagon with suggestions for how to complete the word, similar to what you see on telephone dictionaries. We used the large Danish text corpus ‘' containing 56 million words collected between 1990 and 2000 [9]. This allowed us to speed up spelling and come closer to 2 words per minute with no loss of accuracy.

The use of a BCI is rapidly increasing these years based on low cost, ease of use (e.g. wireless BCI) and more and more applications. It is important to keep on improving the reliability of BCI to meet the moment-to-moment needs of the user [10]. While we previously thought of medical BCI as a PAT for few severely handicapped patients, it looks as if we are going in the direction of BCI as a natural empowerment and interface in rehabilitation of a large number of people suffering a stroke and other acute neurological conditions.

Future BNCI: A roadmap for future directions in brain/neuronal computer interaction research. 2012.
Vuckovic A, Sepulveda F: Quantification and visualisation of differences between two motor tasks based on energy density maps for brain-computer interface applications. Clin Neurophysiol 2008;119:446-458.
Daly JJ, Hogan N, Perepezko EM, Krebs HI, Rogers JM, Goyal KS, et al: Response to upper-limb robotics and functional neuromuscular stimulation following stroke. J Rehabil Res Dev 2005;42:723-736.
Daly JJ, Cheng R, Rogers J, Litinas K, Hrovat K, Dohring M: Feasibility of a new application of noninvasive Brain Computer Interface (BCI): a case study of training for recovery of volitional motor control after stroke. J Neurol Phys Ther 2009;33:203-211.
Galvin R, Murphy B, Cusack T, Stokes E: The impact of increased duration of exercise therapy on functional recovery following stroke - What is the evidence? Top Stroke Rehabil 2008;15:365-377.
Silvoni S, Ramos-Murguialday A, Cavinato M, Volpato C, Cisotto G, Turolla A, et al: Brain-computer interface in stroke: a review of progress. Clin EEG Neurosci 2011;42:245-252.
Pichiorri F, De Vico Fallani F, Cincotti F, Babiloni F, Molinari M, Kleih SC, et al: Sensorimotor rhythm-based brain-computer interface training: the impact on motor cortical responsiveness. J Neural Eng 2011;8:025020.
Muller KR, Tangermann M, Dornhege G, Krauledat M, Curio G, Blankertz B: Machine learning for real-time single-trial EEG-analysis: from brain-computer interfacing to mental state monitoring. J Neurosci Methods 2008;167:82-90.
Det Danske Sprog- og Litteraturselskabs tekstkorpus. 2008.
Shih JJ, Krusienski DJ, Wolpaw JR: Brain-computer interfaces in medicine. Mayo Clin Proc 2012;87:268-279.

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