Cognition in action: Imaging brain/body dynamics in mobile humans

We have recently developed a mobile brain imaging method (MoBI), that allows for simultaneous recording of brain and body dynamics of humans actively behaving in and interacting with their environment. A mobile imaging approach was needed to study cognitive processes that are inherently based on the use of human physical structure to obtain behavioral goals. This review gives examples of the tight coupling between human physical structure with cognitive processing and the role of supraspinal activity during control of human stance and locomotion. Existing brain imaging methods for actively behaving participants are described and new sensor technology allowing for mobile recordings of different behavioral states in humans is introduced. Finally, we review recent work demonstrating the feasibility of a MoBI system that was developed at the Swartz Center for Computational Neuroscience at the University of California, San Diego, demonstrating the range of behavior that can be investigated with this method. Copyright © 2011 by Walter de Gruyter, Berlin, Boston

A Review of fMRI as a Tool for Enhancing Eeg-Based Brain-Machine Interfaces

Human-robot interaction has been going stronger and stronger, up to find a notorious level on brain-machines interfaces. This assistive technology offers a great hope for patients suffering severe neuromuscular disorders. Starting from the current limitations hindering its extensive application outside the research laboratories, this paper reviews findings and prospects on functional magnetic resonance imaging showing how fMRI can help to overcome those limitations, while playing a key role on improving the development of brain-machine interfaces based on electroencephalography. The different types of derived benefits for this interfaces, as well as the different kinds of impact on their components, are presented under a field classification that reveals the distinctive roles that fMRI can play on the present context. The review concludes that fMRI provides complementary knowledge of immediate application, and that a greater profit could be obtained from the own EEG signal by integrating both neuroimaging modalities

Hybrid Sensing and Adaptive Control for Direct Brain Actuation of Artificial Limbs

Developing a non-invasive direct brain control of artificial limbs is both challenging and desirable. Such a sensory and control system, if successful, will have a profound impact on the disabled. In this dissertation, we present the design and development of a non-invasive, hybrid sensory system, which uses near-infrared spectroscopy (NIRS) and electroencephalography (EEG) to measure brain activity with simultaneous electromyography (EMG) to provide feedback data in a healthy limb. Through the combination of these sensory techniques, we have successfully trained a control system capable of mapping brain activity onto muscle actuation. The design of a control algorithm capable of automatic reconfiguration to account for changing sensor conditions, selection of an appropriate pre-trained network based on input characteristics, and adaptation to adjust output based on the user\u27s activity are investigated. The selection of an appropriate algorithm and its initial performance using our sensory system are presented and discussed. The sensory and control system are designed for application in artificial limb control for persons who have undergone amputation of an upper-extremity. Actuation of the elbow and wrist are the primary focus of the study, with the intent to expand to forearm torsion and hand grasping in subsequent studies. During the course of the investigation, the additional function of treating phantom limb pain was incorporated into the design, which has also lead to increased sensor resolution requirements

Implantable Neural Probes for Brain-Machine Interfaces - Current Developments and Future Prospects

A Brain-Machine interface (BMI) allows for direct communication between the brain and machines. Neural probes for recording neural signals are among the essential components of a BMI system. In this report, we review research regarding implantable neural probes and their applications to BMIs. We first discuss conventional neural probes such as the tetrode, Utah array, Michigan probe, and electroencephalography (ECoG), following which we cover advancements in next-generation neural probes. These next-generation probes are associated with improvements in electrical properties, mechanical durability, biocompatibility, and offer a high degree of freedom in practical settings. Specifically, we focus on three key topics: (1) novel implantable neural probes that decrease the level of invasiveness without sacrificing performance, (2) multi-modal neural probes that measure both electrical and optical signals, (3) and neural probes developed using advanced materials. Because safety and precision are critical for practical applications of BMI systems, future studies should aim to enhance these properties when developing next-generation neural probes

EEG and ECoG features for Brain Computer Interface in Stroke Rehabilitation

The ability of non-invasive Brain-Computer Interface (BCI) to control an exoskeleton was used for motor rehabilitation in stroke patients or as an assistive device for the paralyzed. However, there is still a need to create a more reliable BCI that could be used to control several degrees of Freedom (DoFs) that could improve rehabilitation results. Decoding different movements from the same limb, high accuracy and reliability are some of the main difficulties when using conventional EEG-based BCIs and the challenges we tackled in this thesis. In this PhD thesis, we investigated that the classification of several functional hand reaching movements from the same limb using EEG is possible with acceptable accuracy. Moreover, we investigated how the recalibration could affect the classification results. For this reason, we tested the recalibration in each multi-class decoding for within session, recalibrated between-sessions, and between sessions. It was shown the great influence of recalibrating the generated classifier with data from the current session to improve stability and reliability of the decoding. Moreover, we used a multiclass extension of the Filter Bank Common Spatial Patterns (FBCSP) to improve the decoding accuracy based on features and compared it to our previous study using CSP. Sensorimotor-rhythm-based BCI systems have been used within the same frequency ranges as a way to influence brain plasticity or controlling external devices. However, neural oscillations have shown to synchronize activity according to motor and cognitive functions. For this reason, the existence of cross-frequency interactions produces oscillations with different frequencies in neural networks. In this PhD, we investigated for the first time the existence of cross-frequency coupling during rest and movement using ECoG in chronic stroke patients. We found that there is an exaggerated phase-amplitude coupling between the phase of alpha frequency and the amplitude of gamma frequency, which can be used as feature or target for neurofeedback interventions using BCIs. This coupling has been also reported in another neurological disorder affecting motor function (Parkinson and dystonia) but, to date, it has not been investigated in stroke patients. This finding might change the future design of assistive or therapeuthic BCI systems for motor restoration in stroke patients