CS229 Project: Classi Cation Of Motor Tasks Based On .

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CS229 Project: Classification of Motor Tasks Basedon Functional NeuroimagingGerald BrantnerMechanical Engineering, Stanford University, Stanford, CA 94305, USA. geraldb@stanford.eduGeorg SchorppManagement Science and Engineering, Stanford University, Stanford, CA 94305, USA. gschorpp@stanford.eduBrain-machine interfaces (BMIs) aim to establish a new way of communication between humans andcomputers. Especially paralyzed individuals could greatly benefit from BMIs. Currently, most successfulsystems rely on the implantation of electrodes on the motor cortex, but due to its invasive nature, thistechnique prohibits extensive research on human subjects. For this reason, a new approach is needed. Asfunctional magnetic resonance imaging (fMRI) is human-safe, research using this method can be performedat a higher scale. Because brain structure and activity varies among individuals, machine learning is anessential tool to calibrate and train these interfaces. In this project we developed binary and multi-classclassifiers, labeling a set of 10 performed motor tasks based on recorded fMRI brain signals. Our binaryclassifier achieved an average accuracy of 93% across all pairwise tasks and our multi-class classifier yieldedan accuracy of 68%. We demonstrated that combining fMRI and machine learning is a viable path forresearch on BMIs.Key words : Machine Learning, Functional Magnetic Resonance Imaging (fMRI), Brain-Machine Interface1.IntroductionBrain signals of human and non-human primates have previously been translated to control mousecursors [1, 2, 3], keyboard inputs [4], and to guide a robotic hand [5, 6]. Most successful systemsrely on electrodes that are implanted in the brain. Electrodes, however, are invasive, which preventsthe use on human subjects. Functional magnetic resonance imaging (fMRI) is a very promisingalternative [7] because it is (1) non-invasive, (2) does not rely on ionizing radiation, and (3) has highspatial and temporal resolution, which makes it a safe method for research using human subjects.In this research we connect motor tasks with neural activity, in order to classify a subject’s motorstates based on observed brain signals using fMRI.2.Data Collection and PreprocessingWe used data from a study conducted at the Stanford Center for Cognitive and NeurobiologicalImaging (CNI) by GB (author), SM, and CA (Acknowledgements). The experiment required thetest subject to perform a set of ten different tasks (Table 1). The subject repeated each task 121

Brantner, Schorpp:CS229 Project: Classification of Motor Tasks Based on Functional Neuroimaging2Task # DescriptionWrist - Up Down1Wrist - Rotate2Elbow - Up Down3Shoulder - Up Down4Shoulder - Rotate5Task # DescriptionWrist - Up Down Weighted6Wrist - Rotate Weighted7Elbow - Up Down Weighted8Shoulder - Up Down Weighted9Shoulder - Rotate Weighted10Table 1Set of TasksBOLD fMRI time series for voxel 1BOLD fMRI time series for voxel k9task10activation, normalized150100activation, sk8task9task10100505000 50 100051015time [s]Figure 1202530 50051015time [s]202530Sample fMRI BOLD signal for 2 voxels and varying taskstimes and the tasks were randomly ordered. The fMRI scanner partitions the brain into 120,000voxels (cubes of volume 2.5mm3 ). For each of these voxels we simultaneously recorded the fMRIBOLD [8] signal, which measures oxygen consumption due to activation at a temporal resolutionof one second (Figure 1). The raw signals were preprocessed using a standard pipeline.3.Support Vector MachinesIn this section we describe the implementation of our classifier based on Support Vector Machines.We employed MATLAB’s integrated svmtrain and svmclassify functions. By default, the trainingfunction normalizes the data so that it is centered at its mean and has unit standard deviation. Wefound that a linear kernel performs very well for this problem and we chose Sequential MinimalOptimization (SMO) as an optimization method.3.1Initial Feature SelectionWe faced two problems while classifying motor states with fMRI data: First, the feature size isvery large, due to the large number of voxels, and second, the number of samples is much lowerthan the number of features. The low number of samples is a result of the time constraints andoperational cost associated with the MR scanner. We performed preliminary tests and found thatthe most successful features are the concatenated time series of a subset of the recorded voxels(Figure 2). We used a two-stage selection process:

Brantner, Schorpp:CS229 Project: Classification of Motor Tasks Based on Functional Neuroimaging31. Filter by region of interest (ROI): For the scans, we only selected voxels that are part of thebrain’s motor cortex.2. The voxels were ranked by an FIR model’s reliability at capturing variance in BOLD signalresponses to tasks. The top 5000 voxels were selected.3.2Binary SVMWe first used a binary SVM to pair-wise classify all combinations of tasks. For each pair we use 24data points, 12 for each task. The parameters for the algorithm are the number of voxels consideredas well as the length of the input signal. The algorithm uses leave-one-out cross validation for everypair and computes the overall mean accuracy, which reaches 93%. Figure (3) shows the individualclassification accuracy across all pairs. We can see that the accuracy is decreased for similar tasks,especially for a task and its weighted counterpart, (green ellipsoids). This is expected and furthervalidates this approach.3.3Multi Class SVM - One vs OneNext we developed a multi-class SVM algorithm that classifies across all 10 tasks at the sametime. We found that one-vs-one yields the best results compared to other methods, such as one-vsall (Section 4.2). For each test point, this algorithm applies binary classification over all possible300voxel 1voxel 2voxel 3voxel 42001000 10002040Figure 2Figure 360Feature no80100Illustration of a data pointConfusion Matrix for 66 / 110 voxels and 13 sec duration120

Brantner, Schorpp:CS229 Project: Classification of Motor Tasks Based on Functional Neuroimaging4combinations and assigns a point to the winning class. Eventually the test point is classified tothe class scoring the most points. This method in its standard implementation, however, does notaccount for ties. Our enhanced method instead applies another (binary / multi class) classificationbetween the tied task types to make the final decision.3.4Heuristic Feature Selection EnhancementAfter implementing both the binary and multi class SVM we found that using all available data, i.e.5000 voxels over a 30 sec time window, does not lead to the best predictions. Instead, consideringonly the 60-120 most significant voxels over the first 10-15 seconds of the task execution leads tomuch better and more robust results (Figures 4 and 5). The graphs illustrate that the SVMs aremore sensitive to choosing the right time window than to choosing the number of voxels.Mean accuracy binary SVM vs voxel and time; low resolutionMean accuracy binary SVM vs voxel and time; medium .9122025000.752000# of voxels# of 0.61200.8751015time intervalFigure 420100257891011time interval12131415Grid search for binary svm: mean accuracy vs #voxels and tAccuracy multi class SVM vs voxel and time; low resolutionAccuracy multi class SVM vs voxel and time, medium 535000.620.50.4525000.4# of voxels# of 0.520.255000.50.251015time intervalFigure 52025507891011time interval1213Grid search for multi-class svm: accuracy vs #voxels and t1415

Brantner, Schorpp:CS229 Project: Classification of Motor Tasks Based on Functional Neuroimaging4.5Comparison to other ApproachesAs discussed earlier, binary SVM and one-vs-one multi-class SVM turned out to be the best choicecompared to other approaches tested, which we describe in this section.4.1Binary Logistic Regression ClassifierWe also implemented a binary logistic regression classifier, similar to the method described inSection 3.2 and found it to perform 15% less accurate.4.2Multi Class SVM - One vs AllAs an alternative to the one-vs-one multi-class classifier (Section 3.3), we tested one-vs-all. Onevs-one achieves an accuracy of up to 68%, whereas one-vs-all only performs slightly better thanrandom classification.5.ConclusionIn this study we developed binary and multi-class classifiers to label performed motor tasks basedon recorded neural activity using fMRI. On average, we achieved 93% accuracy for the binary caseand 68% for the multi-class case using optimal parameters. Compared to the other approachestested, SVM proofed to be the superior method. Based on these results developing a brain-machineinterface using fMRI is feasible.AcknowledgmentsWe thank Samir Menon for contributing with data collection, preprocessing, and for providing valuableadvice throughout the project. We thank Chris Aholt for volunteering as a subject during the fMRI scans.References[1] P.R. Kennedy, R.A.E. Bakay, M.M. Moore, K. Adams, and J. Goldwaithe. Direct control of a computer from thehuman central nervous system. Rehabilitation Engineering, IEEE Transactions on, 8(2):198–202, 2000.[2] M.D. Serruya, N.G. Hatsopoulos, L. Paninski, M.R. Fellows, and J.P. Donoghue. Brain-machine interface: Instantneural control of a movement signal. Nature, 416(6877):141–142, 2002.[3] E.C. Leuthardt, G. Schalk, J.R. Wolpaw, J.G. Ojemann, and D.W. Moran. A brain–computer interface usingelectrocorticographic signals in humans. Journal of neural engineering, 1(2):63, 2004.[4] G. Santhanam, S.I. Ryu, M.Y. Byron, A. Afshar, and K.V. Shenoy. A high-performance brain–computer interface.nature, 442(7099):195–198, 2006.[5] L.R. Hochberg, M.D. Serruya, G.M. Friehs, J.A. Mukand, M. Saleh, A.H. Caplan, A. Branner, D. Chen, R.D.Penn, and J.P. Donoghue. Neuronal ensemble control of prosthetic devices by a human with tetraplegia. Nature,442(7099):164–171, 2006.[6] J.M. Carmena, M.A. Lebedev, R.E. Crist, J.E. O’Doherty, D.M. Santucci, D.F. Dimitrov, P.G. Patil, C.S. Henriquez, and M.A.L. Nicolelis. Learning to control a brain–machine interface for reaching and grasping by primates.PLoS biology, 1(2):e42, 2003.[7] J.H. Lee, J. Ryu, F.A. Jolesz, Z.H. Cho, and S.S. Yoo. Brain–machine interface via real-time fmri: preliminarystudy on thought-controlled robotic arm. Neuroscience letters, 450(1):1–6, 2009.[8] S. Ogawa, TM Lee, AR Kay, and DW Tank. Brain magnetic resonance imaging with contrast dependent on bloodoxygenation. Proceedings of the National Academy of Sciences, 87(24):9868–9872, 1990.

essential tool to calibrate and train these interfaces. In this project we developed binary and multi-class classi ers, labeling a set of 10 performed motor tasks based on recorded fMRI brain signals. Our binary classi er achieved an average accuracy of 93% across all pairwise tasks and our multi-class classi er yielded an accuracy of 68%.

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