Chaitanya Nutakki

Neural Control using EEG as a BCI Technique for Low Cost Prosthetic Arms

There have been significant advancements in brain computer interface (BCI) techniques using EEG-like methods. EEG can serve as non-invasive BMI technique, to control devices like wheelchairs, cursors and robotic arm. In this paper, we discuss the use of EEG recordings to control low-cost robotic arms by extracting motor task patterns and indicate where such control algorithms may show promise towards the humanitarian challenge. Studies have shown robotic arm movement solutions using kinematics and machine learning methods. With iterative processes for trajectory making, EEG signals have been known to be used to control robotic arms. The paper also showcases a case-study developed towards this challenge in order to test such algorithmic approaches. Non-traditional approaches using neuro-inspired processing techniques without implicit kinematics have also shown potential applications. Use of EEG to resolve temporal information may, indeed, help understand movement coordination in robotic arm.

Classification of robotic arm movement using SVM and Naïve Bayes classifiers

Target-oriented approaches have been commonly used in robotics. In 3D space, movement of a robotic arm depends on the target position which can either follow a forward or inverse kinematics approach to reach the target. Predicting the movement of a robotic arm requires prior learning through methods such as transformation matrices or other machine learning techniques. In this paper, we built an online robotic arm to extract movement datasets and have used machine learning algorithms to predict robotic arm articulation. For efficient training, small training datasets were used for learning purpose. Classification is used as a scheme to replace prediction-correction approach and to test whether the method can function as a replacement of usual forward kinematics schemes or predictor-corrector methods in directing a remotely controlled robotic articulator. This study reports significant classification accuracy and efficiency on real and synthetic datasets generated by the device. The study also suggests linear SVM and Naïve Bayes algorithms as alternatives for computational intensive learning schemes while predicting articulator movement in laboratory environments.

Low-cost robotic articulator as an online education tool: Design, deployment and usage

Humanitarian challenges in developing nations such as low cost prosthesis for the physically challenged, have also led to substantial progress in robotics. In this paper, we implemented and deployed a low-cost remotely controlled robotic articulator, as an education tool for university students and teachers. This tool is freely available online and is being employed to generate robotic datasets for novel algorithms. Using a server-client methodology and a browser-based user interface, the online lab allows learners to access and perform basic kinematics experiments and study robotic articulation. These experiments were developed for allowing students to enhance laboratory skills in robotics and improve practical experience without concerns for equipment access restrictions or cost.

Classifying gait features for stance and swing using machine learning

Structured gait patterns are currently used as a biometric technique to recognize individuals and in building appropriate exoskeleton technologies. In this study, the features involved in gait were extracted and analyzed. Multiple accelerometers were used to collect the data which was then used to identify gait at various axial positions form healthy volunteers with total of 60 trails. Using machine learning optimal feature sub-selection we analyze data to implicate the optimal methods for analysis of swing phase and stance phase in a closed room environment. Study reports that the accelerometer data could classify based on the accuracy and the efficiency of the learning algorithms. Through feature ranking, results suggest gait can be attributed to a combination of Brachium of arm, Antecubitis, Carpus, Coxal, Femur and Tarsus (Shoulder, Elbow, Wrist, Hip, Knee, and Ankle). This gait study may help analyzing conditions during control and movement-related disease.

Mathematical Modelling of Cerebellar Granular Layer Neurons and Network Activity: Information Estimation, Population Behaviour and Robotic Abstractions

Recent studies show cerebellum having a crucial role in motor coordination and cognition, and it has been observed that in patients with movement disorders and other neurological conditions cerebellar circuits are known to be affected. Simulations allow insight on how cerebellar granular layer processes spike information and to understand afferent information divergence in the cerebellar cortex. With excitation-inhibition ratios adapted from in vitro experimental data in the cerebellum granular layer, the model allows reconstructing spatial recoding of sensory and tactile patterns in cerebellum. Granular layer population activity reconstruction was performed with biophysical modeling of fMRI BOLD signals and evoked local field potentials from single neuron and network models implemented in NEURON environment. In this chapter, evoked local field potentials have been reconstructed using biophysical and neuronal mass models interpreting averaged activity and constraining population behavior as observed in experiments. Using neuronal activity and correlating blood flow using the balloon and modified Windkessel model, generated cerebellar granular layer BOLD response. With the focus of relating neural activity to clinical correlations such models help constraining network models and predicting activity-dependent emergent behavior and manifestations. To reverse engineering brain function, cerebellar circuit functions were abstracted into a spiking network based trajectory control model for robotic articulation.

Computational reconstruction of fMRI-BOLD from neural activity

In this paper, we model function magnetic resonance imaging signals generated by neural activity (fMRI). fMRI measures changes in metabolic oxygen in blood in brain circuits based on changes in biophysical factors like concentration of total cerebral blood flow, oxy-hemoglobin and deoxy-hemoglobin content. A modified version of the Windkessel model by incorporating compliance has been used with a balloon model to generate cerebellar granular layer and visual cortex blood oxygen-level dependent (BOLD) responses. Spike raster patterns were adapted from a biophysical granular layer model as input. The model fits volume changes in blood flow to predict the BOLD responses in the cerebellum granular layer and in visual cortex. As a comparison, we tested the balloon model and the modified Windkessel model with the mathematically reconstructed BOLD response under the same input condition. Delayed compliance contributed to BOLD signal and reconstructed signals were compared to experimental measurements indicating the usability of the approach. The current study allows to correlate dynamic changes of flow and oxygenation during brain activation which connects single neuron and network activity to clinical measurements.