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Martina Corazzol Master Thesis

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Martina Corazzol Master Thesis Aalborg University, Biomedical Engineering and Informatics, Spring Semester, 2012 Master Thesis "Classification of movements of the rat based on intra-cortical signals using artificial neural network and support vector machine" Martina Corazzol Department of Health Science & Technology Frederik Bajers Vej 7D2 9220 Aalborg Telephone (+45) 9940 9940 Fax (+45) 9815 4008 http://www.hst.aau.dk Title: Synopsis: "Classification of movements of the rat based on intra-cortical signals using artifi- cial neural network and support vector ma- Background chine" The goal of intra-cortical brain computer in- terface (BCI) is to restore the lost function- alities in disabled patients suffering from Project period: severely impaired movements. BCIs have the Master Thesis, Spring 2012 advantage to create a direct communication pathway between the brain and an external Project group: device for restoring disability. This is pos- Group 12gr1085 sible by decoding signals from the primary motor cortex and translating them into com- Members: mands for a prosthetic device. The project Martina Corazzol aim was to develop a decoding method based on a rat model. Previously recorded data and an already develop pre-processing method were used. The experimental design Supervisor: was developed starting from intra-cortical Winnie Jensen (IC) signal recorded in the rat primary mo- tor cortex (M1). The data pre-processing included denoising with wavelet technique, Assistant Supervisor: spike detection, and feature extraction. After Sofyan Hammad Himaidan Hammad the firing rates of intra-cortical neurons were extracted, artificial neural network (ANN) and support vector machine (SVM) were ap- plied to classify the rat movements into two No. printed Copies: 4 possible classes, Hit or No Hit. The misclas- sification error rates obtained from denoised No. of Pages: 73 and not denoised data were statistically dif- No. of Appendix Pages: 27 ferent (p<0.05), proving the efficiency of the denoising technique. ANN and SVM pro- Total no. of pages: 100 vided comparable classification errors, rang- ing between 14% and 39%. Completed: 1st of June 2012 The contents of this report is freely accessible, however publication (with source references) is only allowed upon agreement with the authors. Preface This report has been composed by the project group 12gr1085, during the 4th semester of the Master of Science in Biomedical Engineering and Informatics with speciality in Medical Sys- tems at the Institute for Health Science and Technology at Aalborg University, Denmark. This project is inserted in an ongoing study about decoding algorithm for characterizing and predicting primary motor cortex (M1) responses during a behavioural task. Therefore, the data recorded during the previous study were used in this project to classify the movements of the rat by using artificial neural network and support vector machine. The reference style used in the report is according to the Harvard method [Last name, Year]. The references are indicated before and after a full stop. If a reference is indicated before a full stop it refers only to the sentence, whereas if it stands after a full stop it refers to the entire section. The references listed in succession are arranged by the year starting with the oldest one. Figures and tables are numbered with reference to the chapter e.g. figure 1 in chapter 2 is "Figure 2.1". The captions are set below the figures or tables. I would like to thank my co-supervisor Sofyan Hammad for the data he provides me and for the assistance during the data processing. Moreover, I would like to thank my supervisor Win- nie Jensen for the technical assistance during the project. This report is prepared by: Martina Corazzol 5 Acronyms BCI Brain Computer Interface BMI Brain Machine Interface CNS Central Nervous System ANN Artificial Neural Network SVM Support Vector Machine M1 Primary Motor Cortex S1/S2 Somatosensory Area 1 and 2 PMA Premotor Area, d dorsal and v ventral SMP Supplementary Motor Area RFA Rostral Forelimb Area IC Intra-Cortical Signal CM Corticomotor neurons fMRI Functional Magnetic Resonance Imaging fNIRS Functional Near-Infrared Spectroscopy MEG Magnetoencephalogram EEG Electroencephalogram ECoG Electrocorticogram LFP Local Field Potential SUA Single Unit Activity MUA Multi Units Activity 6 Table Of Contents Table Of Contents 7 Chapter 1 Introduction 11 1.1 Initiating problem formulation . 12 I Problem Analysis 13 Chapter 2 Elements of a BCI 15 2.1 Principal components of a BCI . 15 2.2 The motor cortex . 17 2.2.1 The primary motor cortex . 18 2.2.2 The premotor cortex . 18 2.2.3 The supplementary motor area . 19 2.2.4 Role of the motor cortex in voluntary movement . 20 2.2.5 Area of interest for the implantation . 21 2.3 Data acquisition: recording techniques . 22 2.3.1 Non-invasive techniques . 22 2.3.2 Invasive techniques . 23 2.4 Neural coding principles and parameter extraction . 24 2.4.1 Single-unit and multi-unit recordings . 24 2.5 External devices and classification . 26 2.5.1 Previous animal experiments . 26 2.5.2 Primates . 26 2.5.3 Rodents . 28 2.5.4 Prediction algorithms . 29 2.6 Feedback and plasticity . 30 2.6.1 Motor imagery . 30 2.6.2 Plasticity . 30 2.7 The rat as a model . 30 2.7.1 Cortical organization in the rat . 31 2.7.2 The motor cortex of the rat . 31 2.7.3 Impairments . 33 2.7.4 Reaching Movement . 33 2.8 Summary of the problem analysis . 33 Chapter 3 Problem Formulation 35 3.1 Problem formulation . 35 7 3.2 Limitations . 35 3.2.1 Development of a real time algorithm and external control device . 35 3.3 Solution strategy . 35 II Problem Solution 37 Chapter 4 Experimental Protocol 39 4.1 Behavioural training . 39 4.2 Materials . 40 4.3 Experimental setup . 40 4.4 Implant procedure . 41 4.5 Recordings . 42 4.6 Good channels detection . 43 Chapter 5 Data Analysis 45 5.1 Data processing algorithm . 45 5.2 Time windows: Hit and NoHit data . 45 5.3 Denoising . 46 5.4 Spike detection . 47 5.5 Feature extraction . 48 5.6 Artificial neural network design . 49 5.6.1 Input vector and target vector . 49 5.6.2 Built the network . 50 5.6.3 Dividing the data . 52 5.6.4 Training the network . 53 5.6.5 Number of neurons in the hidden layer . 53 5.7 Summary of ANN design choices . 55 5.8 Support vector machine classification . 56 5.9 Summary of the SVM design choices . 57 5.10 Evaluating classifier performance . 57 5.11 Misclassification error rate . 58 Chapter 6 Results 59 6.1 Classify denoised and not denoised data with ANN . 59 6.2 Classify denoised and not denoised data with SVM . 61 6.3 Comparison between ANN and SVM classifiers . 63 Chapter 7 Discussion 65 7.1 Methodological considerations . 65 7.2 Data Analysis . 66 7.3 Results . 67 7.4 BCI improvements . 67 7.5 Future prospectives . 68 Chapter 8 Conclusion 69 Bibliography 71 8 TABLE OF CONTENTS A Appendix 75 A.1 The Cerebral Cortex . 75 A.1.1 Lobes . 75 A.1.2 Layers . 76 B Appendix 79 B.1 Artificial neural network . 79 B.2 The neuron . 80 B.3 Models of the neuron . 80 B.4 McCuoolgh and Pitts neural networks . 82 B.5 Gradient Descendent Methods . 83 B.6 Different types of neural network . 83 B.7 Backpropagation . 84 B.8 Initializing Weight . 84 B.9 Feedforward network . 84 B.10 Training the network . 85 C Appendix 87 C.1 Support vector machine . 87 D Appendix 92 D.1 Additional tables and results . 92 9 Introduction 1 Traumatic lesions of the central nervous system (CNS), as well as neurodegenerative disorders, such as amyotrophic lateral sclerosis (ALS), brain stem stroke, muscular dystrophy, cerebral palsy (CP) or "locked-in" syndrome are causes of severe motor deficits in a large number of patients. Every year, spinal cord alone is responsible for the occurrence of 11,000 new cases of paralysis in the United States alone. These cases have to be summed to the over 200,000 es- timated patients who have to cope with partial (paraplegic) or almost total (i.e., quadriplegic) body paralysis only in.
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