Chapter 8 Neurofeedback and Brain–Computer Interface: Clinical Applications

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Most of the research devoted to BMI development consists of methodological studies comparing different online mathematical algorithms, ranging from simple linear discriminant analysis (LDA) (Dornhege et al., 2007) to nonlinear artificial neural networks (ANNs) or support vector machine (SVM) classification. Single cell spiking for the reconstruction of hand movements requires different statistical solutions than electroencephalography (EEG)‐rhythm classification for communication. In general, the algorithm for BMI applications is computationally simple and differences in classification accuracy between algorithms used for a particular purpose are small. Only a very limited number of clinical studies with neurological patients are available, most of them single case studies.

The clinical target populations for BMI‐treatment consist primarily of patients with amyotrophic lateral sclerosis (ALS) and severe CNS damage including spinal cord injuries and stroke resulting in substantial deficits in communication and motor function. However, an extensive body of literature started in the 1970s using neurofeedback training. Such training implemented to control various EEG‐measures provided solid evidence of positive effects in patients with otherwise pharmacologically intractable epilepsy, attention deficit disorder, and hyperactivity ADHD. More recently, the successful introduction and testing of real‐time fMRI and a NIRS‐BMI opened an exciting field of interest in patients with psychopathological conditions.

Introduction

Most clinical applications of BMI‐research rest on the tradition of neurofeedback and biofeedback, both consequences of technological achievements in rapid computer analysis of EEG patterns that allow online feedback and reward of different types of neuroelectric activity (Elbert et al., 1984). BMIs aimed at restoration of movement, however, were built in the tradition of tuning functions of sensori‐motor neurons representing different directions of movements (Georgopoulos et al., 2007).

Neurofeedback allowed, for the first time, voluntary self‐regulation of brain activity through feedback and reward. Expectancies ran high and many premature announcements of clinical success based on single case studies or uncontrolled observations discredited the field early on. In the 1970s Miller's demonstrations of operant control of autonomic (and CNS) functions (Miller, 1969) in curarized rats, supposedly proving “voluntary” operant regulation of many bodily functions excluding mediation of the motor system through curarization, turned out to be difficult to replicate (Dworkin and Miller, 1986). Together with the clinical overstatements in the field of biofeedback, this historic incident virtually halted funding from public sources and blocked large controlled clinical studies despite some indications of its efficiency. However, more recent studies suggested that some patients with drug‐resistant epilepsy (mostly with secondarily generalized seizures) experienced a reduction in the number of ictal events during and after training consistent of self‐regulation of slow cortical potentials (SCPs) (Kotchoubey et al., 2001, Rockstroh et al., 1993), an effect also reported using biofeedback of skin conductance responses (GSR) (Nagai et al., 2004). Nagai et al. showed that learned increase in autonomic arousal through reduction of skin conductance decreased negative SCPs at the cortical level and thus increased seizure thresholds confirming earlier reports (Birbaumer et al., 1990, Kotchoubey et al., 2001, Rockstroh et al., 1993).

In those studies with training and visual feedback of positive SCPs in focal epilepsies, some patients achieved virtually 100% accuracy in the control of SCPs after extensive training of 30–50 sessions, thus paving the way for application to BMIs for communication. Still, well‐controlled trials with larger samples of epileptic patients have not been implemented.

Another promising line of neurofeedback in neurology is the self‐regulation of SCPs and mu‐rhythm (also called sensori‐motor‐rhythm, SMR) in attention deficit disorder and hyperactivity (ADHD). SMR occurs over the sensorimotor rolandic brain regions with a frequency of 8–15 Hz indicating motor quiescence and a functionally inhibitory mode of the thalamocortical loops (Sterman and Clemente, 1962a). Motor imagery or motor action desynchronizes SMR (event‐related desynchronization, ERD). Well‐controlled studies with relatively small samples of ADHD children showed potential, pointing to lasting effects on attention, and vigilance comparable to those achieved through pharmacological treatment with stimulants (Fuchs et al., 2003, Strehl et al., 2006). All in all, these pioneering studies underlined the possibility to control human electrocortical activity and to modify motor and cognitive functions in health and disease.

Section snippets

Functional Magnetic Resonance Imaging: fMRI‐BMI

Near‐infrared‐spectroscopy (NIRS) measuring changes in oxygenation and in deoxygenation of the cortical surface is a relatively cheap noninvasive technology whose regulation can be learned within a few training sessions with contingent feedback only (Sitaram et al., 2007). Sitaram et al. trained healthy human subjects successfully to maximize the difference between right and left sensorimotor regions.

The regulation of the blood oxygenation level dependent (BOLD) response with real‐time fMRI

BMI in Locked‐in Syndrome

Patients with progressive motor neuron disease, particular amyotrophic lateral sclerosis (ALS), Guillain–Barré Syndrome, and subcortical stroke, as well as patients with traumatic brain damage in vegetative state (Kotchoubey, 2005) may suffer from locked‐in syndrome (LIS) or total locked‐in syndrome (TLIS). LIS is defined as complete paralysis with one or a few voluntary functions left (usually small eye movements). TLIS consists of complete cessation of volitional control of all voluntary

BMI in Stroke and Spinal Cord Injury

Experimentation with nonhuman primates suggests that intentional goal‐directed movements of the upper limbs can be reconstructed and transmitted to external manipulandum or robotic devices controlled from a relatively small number of microelectrodes implanted into movement‐relevant brain areas after some training, opening the door for the development of brain–computer interfaces (BCIs) or brain–machine interfaces (BMIs) in humans. While noninvasive BMIs using electroencephalographic recordings

Conclusion

Despite a growing animal literature demonstrating online control of functional hand movements from spike patterns recorded with microelectrodes in the motor cortex, BMI applications in neurological patients are rare and hampered by methodological difficulties. BMIs using EEG‐measures allow verbal communication in paralyzed patients with ALS, BMI‐communication in totally locked‐in patients, however, awaits experimental confirmation. Movement restoration in chronic stroke without residual

Acknowledgments

This work was supported by the Deutsche Forschungsgemeinschaft (DFG), Bundesministerium für Bildung und Forschung (BMBF, Bernstein-Center for Neurotechnology 01GQ0831), Fatronik, San Sebastian, Spain, Motorike, Cesarea, Israel. Pedro Montoya was supported by Spanish Ministry of Science and European Funds (FEDER) (grant SEJ2007–62312).

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