Current research on Brain-Computer Interface (BCI) controllers has expanded the opportunities of robotic applications within the biomechanical field. With the implementation of a real-time BCI controller, researchers have developed smart prosthetics, semi-autonomous wheelchairs, and collaborative robots for human interactions, allowing patients with neuromuscular disabilities the freedom to interact with the world. These advances have been made possible through the ease of non-invasive procedures for recording and processing electroencephalography (EEG) signals from the human scalp. However, EEG based BCI controllers are limited in their ability to accurately process real-time signals and convert them into input for a system. This research focuses on the development of a hybrid-BCI controller for a semi-autonomous three-wheeled omnidirectional robot capable of processing accurate real-time commands. EEG scans are recorded utilizing a fourteen-electrode channel cap provided by Easycap utilizing modified Emotiv Epoc hardware. Signals are recorded and processed by a program called OpenViBE in which users respond to different stimulus events. A MATLAB plugin, called BCILAB, is used to clean and process the data. This data is used to train the hybrid-BCI controller to be capable of differentiating between hand and foot motor imagery (MI) as well as jaw electromyography (EMG) signals. Once identified, the controller converts the signal into input commands of {forward, backward, left, right, rotate, stop}, which are published over LabStreamingLayer (LSL) to the robot. To date, omnidirectional mobile robots are popularly employed for their holonomic abilities, meaning they have three degrees of freedom (DoF) and are capable of traversing through its environment in any orientation. As such, a holonomic robot is proposed. The system is equipped with the Intel RealSense Depth Camera D435, as well as Lidar sensors to build a full map of the robot's surroundings. Robot operations are completed on the NVIDIA Jetson Xavier which runs the Robot Operating System (ROS). ROS manages all aspects of robot operations, called nodes. This includes receiving and translating BCI inputs, reading all sensor data, computing a trajectory and navigating the robot along the trajectory. Prototyping and developmental work was performed by creating a model of the robot in the Unified Robot Description Format (URDF) which can be run in Gazebo, a simulation software with a realistic physics model. The design of the system controller was tested in this simulated environment for both path planning and obstacle avoidance as well as receiving inputs from the BCI controller. The robot was able complete testing tasks and achieve goals with less than 10% error on average, often experiencing no more than 2% error when considering built in tolerance thresholds

This book constitutes the refereed proceedings of the Second IFIP WG 5.5/SOCOLNET Doctoral Conference on Computing, Electrical and Industrial Systems, DoCEIS 2011, held in Costa de Caparica, Portugal, in February 2011. The 67 revised full papers were carefully selected from numerous submissions. They cover a wide spectrum of topics ranging from collaborative enterprise networks to microelectronics. The papers are organized in topical sections on collaborative networks, service-oriented systems, computational intelligence, robotic systems, Petri nets, sensorial and perceptional systems, sensorial systems and decision, signal processing, fault-tolerant systems, control systems, energy systems, electrical machines, and electronics.

This Research Topic presents bio-inspired and neurological insights for the development of intelligent robotic control algorithms. This aims to bridge the inter-disciplinary gaps between neuroscience and robotics to accelerate the pace of research and development.

This book discusses the basic requirements and constraints in building a brain–computer interaction system. These include the technical requirements for building the signal processing module and the acquisition module. The major aspects to be considered when designing a signal acquisition module for a brain–computer interaction system are the human brain, types and applications of brain–computer systems, and the basics of EEG (electroencephalogram) recording. The book also compares the algorithms that have been and that can be used to design the signal processing module of brain–computer interfaces, and describes the various EEG-acquisition devices available and compares their features and inadequacies. Further, it examines in detail the use of Emotiv EPOC (an EEG acquisition module developed by Emotiv) to build a complete brain–computer interaction system for driving robots using a neural network classification module.

Engineers in the field of control systems have been recently drawn to the development of creating a hands-free and speech-free controller interface over computers and robotic devices. The primary individuals who would use this type of controller suffer from progressive nervous system diseases or other forms of paralysis that have severely restricted any movement of the limbs. Despite their physical limitations, these same individuals have an uncompromised brain full of cognitive and sensory functions. As a result, one solution to restore mobility and autonomy to the paralyzed is to create a controller that utilizes their brain signals. A brain computer interface (BCI) applies brain signals as input to a controller that will then drive a robot arm or transporter. By linking a specific mental task (i.e. imagine squeezing the right hand) to a command a robot (i.e. make a right turn), users have the ability to navigate an electrically powered wheel chair or robot-aid for themselves. While there is potential to create a wide range of controller commands, brainwaves come with their own set of challenges. These signals are non-stationary and non-linear; meaning, brainwaves constantly vary and are extremely difficult to model. In addition, noise from other involuntary functions (i.e. blinking and facial muscle activation) may bury the unique signals associated to the mental task. To overcome these obstacles, control system engineers have implemented a signal preprocessing step and machine learning approach to these controllers. The combination of selecting the right preprocessor, machine learning algorithm, and training the user to conduct clear mental tasks creates an accurate and responsive BCI controller. The main goal of this project is to design a six-class hybrid BCI controller for a semi-autonomous mobile robotic arm. The controller is designed to operate the robotic base and arm separately. To do this, a set of EEG motor imagery hand and feet signals serves two primary functions: they navigate the robot base in the environment and move a cursor on the robot's camera screen to highlight what object to grab. In addition, a jaw clench, which is an electromyogram (EMG) signal, is used to switch between commanding the base and the arm. Designing a controller with this capability for multiple users requires a compilation of hardware to record/stream brainwaves and software to preprocess and train a machine learning algorithm. A modified 14-channel commercial grade non-invasive electroencephalogram (EEG) headset from Emotiv Epoch was used to output the brain waves of three healthy males (ages 22 - 27) to the computer. Each subject recorded five sessions, each with four tests, of their responses to OpenViBE's stimulus presentation program. The recordings were then uploaded to EEGLAB, an open source MATLAB plug-in, where the signals were preprocessed with filters and the implementation of Independent Component Analysis (ICA). Additionally, EEGLAB was used to plot Event Related Potential (ERP) plots and topographical maps to observe each subject's brain activity. After reviewing all the plots, each subject shared the same behavior in electrodes C1, C3, C5, C2, C4, and C6. For comparison, two machine learning algorithms, linear discriminant analysis (LDA) and relevance vector machine (RVM) were chosen to process and classify the subjects' recordings. The performance for each classifier was recorded for a 2-class, 3-class, 5-class, and 6-class controller. RVM out performed LDA with multi-class controllers. For a 5-class controller, the error rate percentages were: 45% for subject S01, 30.8% for subject S02, and 29.2% for subject S03. With the proper electrodes and machine learning algorithms identified, the official 6-class controller was created with a common spatial pattern (CSP) filter and RVM classifier. It was observed that the accuracy of the controller decreased as the number of classes increased. The 6-class BCI controller was integrated into a virtual model of the semi-autonomous robotic arm where it successfully demonstrated the ability to separately move the base, move the cursor on the robot's camera screen, and activate the action to pick up/drop off an object.

This research presents a non-invasive hybrid EEG-based brain computer interface (BCI) system aimed at providing a technological solution for individuals suffering from paralysis or severe motor disability. It also aims to enable individuals to communicate with surrounding environment vita thoughts and stimuli.

Brain-computer interface (BCI) technology provides a means of communication that allows individuals with severely impaired movement to communicate with assistive devices using the electroencephalogram (EEG) or other brain signals. The practicality of a BCI has been possible due to advances in multi-disciplinary areas of research related to cognitive neuroscience, brain-imaging techniques and human-computer interfaces. However, two major challenges remain in making BCI for assistive robotics practical for day-to-day use: the inherent lower bandwidth of BCI, and how to best handle the unknown embedded noise within the raw EEG. Brain-Computer Interfacing for Assistive Robotics is a result of research focusing on these important aspects of BCI for real-time assistive robotic application. It details the fundamental issues related to non-stationary EEG signal processing (filtering) and the need of an alternative approach for the same. Additionally, the book also discusses techniques for overcoming lower bandwidth of BCIs by designing novel use-centric graphical user interfaces. A detailed investigation into both these approaches is discussed. - An innovative reference on the brain-computer interface (BCI) and its utility in computational neuroscience and assistive robotics - Written for mature and early stage researchers, postgraduate and doctoral students, and computational neuroscientists, this book is a novel guide to the fundamentals of quantum mechanics for BCI - Full-colour text that focuses on brain-computer interfacing for real-time assistive robotic application and details the fundamental issues related with signal processing and the need for alternative approaches - A detailed introduction as well as an in-depth analysis of challenges and issues in developing practical brain-computer interfaces.