Human Behavior Recognition Ssing Brain LFP Signal in the Presence of the Stimulation Pulse

Design and Methodology This study concentrates on human behavior classification task using local field potential (LFP) signals recorded from three subjects with Parkinson’s disease (PD). Existing approaches mainly employ the LFP signals acquired under the stimulation/off condition. In practical situations, however, it is necessary to design a classification method capable of recognizing different human activities under the stimulation/on condition, where the classification task is more complicated due to the artifacts imposed by the high amplitude stimulation pulse (~1-3volts). We utilize the time-frequency representation of the acquired LFPs in the Beta frequency range (~10-30Hz) to develop a feature space based on which the classification is efficiently performed while the high frequency stimulation pulse (~130-180Hz) has no/limited impact on the classification performance. Original Data and Results All three participants had undergone DBS surgery with implanted DBS leads (Medtronic 3389, Minneapolis, MN, USA) in the subthalamic nucleus of the brain. The recording sessions required the participants to do several repetitions of designed “button press” and “reach” trials under the condition of stimulation on/off. On average, 60 recordings were performed for each trial. Our analysis on the power spectral density (PSD) of the data showed that the stimulation pulse mostly impacts the frequency components around the stimulation frequency (~140Hz). Using a linear-kernel SVM classifier for classifying the aforementioned trials based on the proposed feature space, we obtained a classification accuracy of ~88% and ~87% respectively for stimulation off and on cases. Conclusion PD incidence increases with advancing age and peaks among people in their 60s and 70s. The cost of PD in the United States is estimated to be $25 billion per year. Thus, advanced techniques to improve the performance of existing devices are highly demanded. Human behavior classification from brain signals is essential in developing the next generation of closed-loop deep brain stimulation (DBS) systems. A closed-loop DBS system that utilizes appropriate physiological control variables may improve therapeutic results, reduce stimulation side effects, and extend battery life of pulse generators

Behavior Classification Using Multi-site LFP and ECoG Signals

Abstract-Deep Brain Stimulation (DBS) is an effective therapy that alleviates the motor signs of Parkinson’s disease (PD). Existing DBS is open loop, providing a time invariant stimulation pulse train that may generate cognitive, speech, and balance side effects. A closed-loop DBS system that utilizes appropriate physiological control variables may improve therapeutic results, reduce stimulation side effects, and extend battery life of pulse generators. Furthermore, by customizing DBS to a patient’s behavioral goal, side effects of stimulation may arise only when they are non-detrimental to the patient’s current goals. Therefore, classification of human behavior using physiological signals is an important step in the design of the next generation of closed-loop DBS systems. Ten subjects who were undergoing DBS implantation were recruited for the study. DBS leads were used to record bilateral STN-LFP activity and an electrocorticography (ECoG) strip was used to record field potentials over left prefrontal cortex. Subjects were cued to perform voluntary behaviors including left and right hand movement, left and right arm movement, mouth movement, and speech. Two types of algorithms were used to classify the subjects’ behavior, support vector machine (SVM) using linear, polynomial, and RBF kernels as well as lp-norm multiple kernel learning (MKL). Behavioral classification was performed using only LFP channels, only ECoG channels, and both LFP and ECoG channels. Features were extracted from the time-frequency representation of the signals. Phase locking values (PLV) between ECoG and LFP channels were calculated to determine connectivity between sites and aid in feature selection. Classification performance improved when multi-site signals were used with either SVM or MKL algorithms. Our experiments further show that the lp-norm MKL outperforms single kernel SVM-based classifiers in classifying behavioral tasks. References [1] H. M. Golshan, A. O. Hebb, S. J. Hanrahan, J. Nedrud, and M. H. Mahoor, “A multiple kernel learning approach for human behavioral task classification using STN-LFP signal,” EMBC, 38th IEEE International Conference on., pp.1030-1033, 2016. [2] H. M. Golshan, A. O. Hebb, S. J. Hanrahan, J. Nedrud, and M. H. Mahoor, “An FFT-based synchronization approach to recognize human behaviors using STN-LFP signal,” To appear in ICASSP, 42nd IEEE International Conference on., 2017

A Wavelet-based Approach to Emotion Classification using EDA Signals

Emotion is an intense mental experience often manifested by rapid heartbeat, breathing, sweating, and facial expressions. Emotion recognition from these physiological signals is a challenging problem with interesting applications such as developing wearable assistive devices and smart human-computer interfaces. This paper presents an automated method for emotion classification in children using electrodermal activity (EDA) signals. The time-frequency analysis of the acquired raw EDAs provides a feature space based on which different emotions can be recognized. To this end, the complex Morlet (C-Morlet) wavelet function is applied on the recorded EDA signals. The dataset used in this paper includes a set of multimodal recordings of social and communicative behavior as well as EDA recordings of 100 children younger than 30 months old. The dataset is annotated by two experts to extract the time sequence corresponding to three main emotions including “Joy”, “Boredom”, and “Acceptance”. The annotation process is performed considering the synchronicity between the children\u27s facial expressions and the EDA time sequences. Various experiments are conducted on the annotated EDA signals to classify emotions using a support vector machine (SVM) classifier. The quantitative results show that the emotion classification performance remarkably improves compared to other methods when the proposed wavelet-based features are used