Decoding Repetitive Finger Movements with Brain Signals Acquired Via Noninvasive Electroencephalography

We investigated how well finger movements can be decoded from electroencephalography (EEG) signals. 18 hand joint angles were measured simultaneously with 64-channel EEG while subjects performed a repetitive finger tapping task. A linear decoder with memory was used to predict continuous index finger angular velocities from EEG signals. A genetic algorithm was used to select EEG channels across temporal lags between the EEG and kinematics recordings, which optimized decoding accuracies. To evaluate the accuracy of the decoder, the Pearson's correlation coefficient (r) between the observed and predicted trajectories was calculated in a 10-fold cross-validation scheme. Our results (median r = .403, maximum r = .704), compare favorably with previous studies that used electrocorticography (ECoG) to decode finger movements. The decoder used in this study can be used for future brain machine interfaces, where individuals can control peripheral devices through EEG signals

A Method for Automatic and Objective Scoring of Bradykinesia Using Orientation Sensors and Classification Algorithms

Correct assessment of bradykinesia is a key element in the diagnosis and monitoring of Parkinson's disease. Its evaluation is based on a careful assessment of symptoms and it is quantified using rating scales, where the Movement Disorders Society-Sponsored Revision of the Unified Parkinson's Disease Rating Scale (MDS-UPDRS) is the gold standard. Regardless of their importance, the bradykinesia-related items show low agreement between different evaluators. In this study, we design an applicable tool that provides an objective quantification of bradykinesia and that evaluates all characteristics described in the MDS-UPDRS. Twenty-five patients with Parkinson's disease performed three of the five bradykinesia-related items of the MDS-UPDRS. Their movements were assessed by four evaluators and were recorded with a nine degrees-of-freedom sensor. Sensor fusion was employed to obtain a 3-D representation of movements. Based on the resulting signals, a set of features related to the characteristics described in the MDS-UPDRS was defined. Feature selection methods were employed to determine the most important features to quantify bradykinesia. The features selected were used to train support vector machine classifiers to obtain an automatic score of the movements of each patient. The best results were obtained when seven features were included in the classifiers. The classification errors for finger tapping, diadochokinesis and toe tapping were 15-16.5%, 9.3-9.8%, and 18.2-20.2% smaller than the average interrater scoring error, respectively. The introduction of objective scoring in the assessment of bradykinesia might eliminate inconsistencies within evaluators and interrater assessment disagreements and might improve the monitoring of movement disorders

The Smartphone Brain Scanner: A Portable Real-Time Neuroimaging System

Combining low cost wireless EEG sensors with smartphones offers novel opportunities for mobile brain imaging in an everyday context. We present a framework for building multi-platform, portable EEG applications with real-time 3D source reconstruction. The system - Smartphone Brain Scanner - combines an off-the-shelf neuroheadset or EEG cap with a smartphone or tablet, and as such represents the first fully mobile system for real-time 3D EEG imaging. We discuss the benefits and challenges of a fully portable system, including technical limitations as well as real-time reconstruction of 3D images of brain activity. We present examples of the brain activity captured in a simple experiment involving imagined finger tapping, showing that the acquired signal in a relevant brain region is similar to that obtained with standard EEG lab equipment. Although the quality of the signal in a mobile solution using a off-the-shelf consumer neuroheadset is lower compared to that obtained using high density standard EEG equipment, we propose that mobile application development may offset the disadvantages and provide completely new opportunities for neuroimaging in natural settings

Enhancing touch interactions with passive finger acoustics

In this report, we introduce a unique method of interacting with mobile devices through passive finger acoustics. Phone users typically use one of two fingers when interacting with a touchscreen device, leaving the other fingers idle. The motivation is that users can make use of their idle fingers to enhance the experience of an application. By wearing minimally obtrusive rings on the thumb and index finger, users can make distinguishable clicking sounds to quickly perform actions, without having to interact directly with the screen. Our system leverages the microphone embedded in the mobile device to capture sound and recognize sound in real-time without requiring Internet connection. The different sounds introduce new ways for users to interact with their devices, without cluttering the screen. We evaluated our system on an Android drawing application, which allows the user to switch tools based on clicks. We optimized our system based on accuracy, classification speed, and ease of use, in order to create a comfortable user experienceInformatio

Functional Near Infrared Detection of Real and Imagined Finger Taps Using Support Vector Machine, Linear Discriminant Analysis, and Decision Tree Classification Methods

This study investigates the thesis that given cerebral response samples of an individual\u27s left, right, both, and imagined finger tapping, continuous wave (CW) functional Near Infrared (fNIR), unregistered with fMRI, can differentiate between any two of the four categories. Fifty subjects were outfitted with a single source/detector attached to a single, square pad, affixed to their heads using devices such as elastic bands and caps for light shielding. Slides depicting arrows pointing left, right, both directions, or made of dashed lines were presented to each subject, with a slide of text interspersed between each. Subjects tapped with their left finger, right finger, both left and right finger, or imagined tapping, depending on the type of arrow. Text was presented in between each tapping slide and was read with no tapping. Each slide was presented for twenty seconds and each type of tapping occurred three times in an eight minute, 20 second period. Classification was performed using Support Vector Machine (SVM), Linear Discriminant Analysis (LDA), and decision tree algorithms. Results indicated that left finger tapping can be distinguished from right, both, and imagined right finger-tapping with error rates ranging from 24.92% to 29.51% (SVM), 40.05% to 42.69% (LDA), and 23.34% to 28.85% (decision tree). The decision tree algorithm produced results, on an individual trial basis, with greater than 95% confidence that the results were not due to chance. These results were obtained with no screening out due to individual characteristics such as hair thickness. The generalizations included the use of a large sample of subjects for which the selection criteria only included statutory minimum and maximum ages. This study also produced validation of a method of mitigating hair effect. Raising the sensor was shown to still produce valid results that could not be attributed to chance at a confidence level of 95%. The results are directly applicable to brain-computer interfaces in a number of areas. These relate to validating the ability to classify data collected by a device with a single source/detector, from non-prescreened individuals, with real-time algorithms in a normal environment

Magnetoencephalography in Stroke Recovery and Rehabilitation

Magnetoencephalography (MEG) is a non-invasive neurophysiological technique used to study the cerebral cortex. Currently, MEG is mainly used clinically to localize epileptic foci and eloquent brain areas in order to avoid damage during neurosurgery. MEG might, however, also be of help in monitoring stroke recovery and rehabilitation. This review focuses on experimental use of MEG in neurorehabilitation. MEG has been employed to detect early modifications in neuroplasticity and connectivity, but there is insufficient evidence as to whether these methods are sensitive enough to be used as a clinical diagnostic test. MEG has also been exploited to derive the relationship between brain activity and movement kinematics for a motor-based brain-computer interface. In the current body of experimental research, MEG appears to be a powerful tool in neurorehabilitation, but it is necessary to produce new data to confirm its clinical utility