Reza Abiri

Tuning Up the Old Brain with New Tricks: Attention Training via Neurofeedback

Neurofeedback (NF) is a form of biofeedback that uses real-time (RT) modulation of brain activity to enhance brain function and behavioral performance. Recent advances in Brain-Computer Interfaces (BCI) and cognitive training (CT) have provided new tools and evidence that NF improves cognitive functions, such as attention and working memory (WM), beyond what is provided by traditional CT. More published studies have demonstrated the efficacy of NF, particularly for treating attention deficit hyperactivity disorder (ADHD) in children. In contrast, there have been fewer studies done in older adults with or without cognitive impairment, with some notable exceptions. The focus of this review is to summarize current success in RT NF training of older brains aiming to match those of younger brains during attention/WM tasks. We also outline potential future advances in RT brainwave-based NF for improving attention training in older populations. The rapid growth in wireless recording of brain activity, machine learning classification and brain network analysis provides new tools for combating cognitive decline and brain aging in older adults. We optimistically conclude that NF, combined with new neuro-markers (event-related potentials and connectivity) and traditional features, promises to provide new hope for brain and CT in the growing older population.

Design, fabrication, and nonlinear control of a flexible minirobot module by using shape memory alloy actuators:

Recently, the increasing need for performing intricate operations in small dimensions has motivated many researchers and industrialists to focus on minirobots. Different types of minirobots with diverse locomotive mechanisms have been designed for various applications. Modular design is a new method which is employed to fabricate small robots with more flexibility and capability. In many works, each module of modular minirobots has rotational displacement. In this article, a flexible minirobot module is developed and manufactured which can produce controlled rotational displacement. Shape memory alloy springs are applied as the actuators to provide impressively large strokes. The final fabricated flexible minirobot module is verified by the open-loop experimental tests. In order to achieve the desired maneuvers and have a perfect tracking of reference input, a nonlinear fuzzy controller is developed and implemented to control the maneuvers of flexible minirobot module. Finally, the results are discussed which show good agreement between the simulations and experimental tests.

A Real-Time Brainwave Based Neuro-Feedback System for Cognitive Enhancement

Brain Computer Interface (BCI) provides a pathway to connect the brain to external devices. Neuro-rehabilitation provides advanced means to assist people with movement disorders such as post-stroke patients and those with lost limbs. While much progress has been made in neuro-rehabilitation as assistive devices, few studies had examined mental rehabilitation assisted by BCI such as memory training using neuroenhancement. It should be noted that many patients with physical disabilities also suffer cognitive difficulties. On the other hand, cognitive decline can also be the result of normal aging without brain injury nor diseases. Here, we propose a novel real-time brainwave BCI platform for enhancing human cognitive by designing and employing a personalized neuro-feedback robot. Short-term memory and attention are among the most important cognitive abilities which manifest in many mental diseases. A social robot is integrated into the BCI system to provide feedback based on individual’s brainwaves and memory performance. As a simple scenario of memory task, real-time EEG signals will be monitored during a visual object memory task. Our novel neuro-feedback system has great potential as a neuro-enhancing device for cognitive rehabilitation.Copyright

Identification and Control of Shape Memory Alloys

In recent years, shape memory alloys have been used widely as actuators in microelectromechanical systems. Shape memory alloys have non-linear hysteresis behaviours and parameter uncertainties that include electrical properties such as resistance. These behavioural uncertainties limit the control accuracy of shape memory alloy actuators using mathematical models. In this article, a new method is proposed for force control of shape memory alloy spring actuators. An artificial neural network is used to identify and control the shape memory alloy actuator. The shape memory alloy spring under test is a product of the Toki Corporation; its coil diameter is 0.62 mm, the diameter of the wire is 0.15 mm, and this type of shape memory alloy actuator can produce 40 gf. The results obtained are verified by an experimental set-up, which is also used to train the artificial neural network as an identifier and a controller of the system.

Real-Time Brain Machine Interaction via Social Robot Gesture Control

Brain-Machine Interaction (BMI) system motivates interesting and promising results in forward/feedback control consistent with human intention. It holds great promise for advancements in patient care and applications to neurorehabilitation. Here, we propose a novel neurofeedback-based BCI robotic platform using a personalized social robot in order to assist patients having cognitive deficits through bilateral rehabilitation and mental training. For initial testing of the platform, electroencephalography (EEG) brainwaves of a human user were collected in real time during tasks of imaginary movements. First, the brainwaves associated with imagined body kinematics parameters were decoded to control a cursor on a computer screen in training protocol. Then, the experienced subject was able to interact with a social robot via our real-time BMI robotic platform. Corresponding to subjects imagery performance, he/she received specific gesture movements and eye color changes as neural-based feedback from the robot. This hands-free neurofeedback interaction not only can be used for mind control of a social robots movements, but also sets the stage for application to enhancing and recovering mental abilities such as attention via training in humans by providing real-time neurofeedback from a social robot.