Design and Validation of a Low-Cost Mobile EEG-Based Brain-Computer Interface.

Objective: We designed and validated a wireless, low-cost, easy-to-use, mobile, dry-electrode headset for scalp electroencephalography (EEG) recordings for closed-loop brain-computer (BCI) interface and internet-of-things (IoT) applications. Approach: The EEG-based BCI headset was designed from commercial off-the-shelf (COTS) components using a multi-pronged approach that balanced interoperability, cost, portability, usability, form factor, reliability, and closed-loop operation. Main Results: The adjustable headset was designed to accommodate 90% of the population. A patent-pending self-positioning dry electrode bracket allowed for vertical self-positioning while parting the user's hair to ensure contact of the electrode with the scalp. In the current prototype, five EEG electrodes were incorporated in the electrode bracket spanning the sensorimotor cortices bilaterally, and three skin sensors were included to measure eye movement and blinks. An inertial measurement unit (IMU) provides monitoring of head movements. The EEG amplifier operates with 24-bit resolution up to 500 Hz sampling frequency and can communicate with other devices using 802.11 b/g/n WiFi. It has high signal-to-noise ratio (SNR) and common-mode rejection ratio (CMRR) (121 dB and 110 dB, respectively) and low input noise. In closed-loop BCI mode, the system can operate at 40 Hz, including real-time adaptive noise cancellation and 512 MB of processor memory. It supports LabVIEW as a backend coding language and JavaScript (JS), Cascading Style Sheets (CSS), and HyperText Markup Language (HTML) as front-end coding languages and includes training and optimization of support vector machine (SVM) neural classifiers. Extensive bench testing supports the technical specifications and human-subject pilot testing of a closed-loop BCI application to support upper-limb rehabilitation and provides proof-of-concept validation for the device's use at both the clinic and at home. Significance: The usability, interoperability, portability, reliability, and programmability of the proposed wireless closed-loop BCI system provides a low-cost solution for BCI and neurorehabilitation research and IoT applications.

A Mixed Filtering Approach for Real-Time Seizure State Tracking Using Multi-Channel Electroencephalography Data.

Real-time continuous tracking of seizure state is necessary to develop feedback neuromodulation therapy that can prevent or terminate a seizure early. Due to its high temporal resolution, high scalp coverage, and non-invasive applicability, electroencephalography (EEG) is a good candidate for seizure tracking. In this research, we make multiple seizure state estimations using a mixed-filter and multiple channels found over the entire sensor space; then by applying a Kalman filter, we produce a single seizure state estimation made up of these individual estimations. Using a modified wrapper feature selection, we determine two optimal features of mixed data type, one continuous and one binary analyzing all available channels. These features are used in a state-space framework to model the continuous hidden seizure state. Expectation maximization is performed offline on the training and validation data sets to estimate unknown parameters. The seizure state estimation process is performed for multiple channels, and the seizure state estimation is derived using a square-root Kalman filter. A second expectation maximization step is utilized to estimate the unknown square-root Kalman filter parameters. This method is tested in a real-time applicable way for seizure state estimation. Applying this approach, we obtain a single seizure state estimation with quantitative information about the likelihood of a seizure occurring, which we call seizure probability. Our results on the experimental data (CHB-MIT EEG database) validate the proposed estimation method and we achieve an average accuracy, sensitivity, and specificity of 92.7%, 92.8%, and 93.4%, respectively. The potential applications of this seizure estimation model are for closed-loop neuromodulation and long-term quantitative analysis of seizure treatment efficacy.

Deep learning for electroencephalogram (EEG) classification tasks: a review.

OBJECTIVE: Electroencephalography (EEG) analysis has been an important tool in neuroscience with applications in neuroscience, neural engineering (e.g. Brain-computer interfaces, BCI's), and even commercial applications. Many of the analytical tools used in EEG studies have used machine learning to uncover relevant information for neural classification and neuroimaging. Recently, the availability of large EEG data sets and advances in machine learning have both led to the deployment of deep learning architectures, especially in the analysis of EEG signals and in understanding the information it may contain for brain functionality. The robust automatic classification of these signals is an important step towards making the use of EEG more practical in many applications and less reliant on trained professionals. Towards this goal, a systematic review of the literature on deep learning applications to EEG classification was performed to address the following critical questions: (1) Which EEG classification tasks have been explored with deep learning? (2) What input formulations have been used for training the deep networks? (3) Are there specific deep learning network structures suitable for specific types of tasks? APPROACH: A systematic literature review of EEG classification using deep learning was performed on Web of Science and PubMed databases, resulting in 90 identified studies. Those studies were analyzed based on type of task, EEG preprocessing methods, input type, and deep learning architecture. MAIN RESULTS: For EEG classification tasks, convolutional neural networks, recurrent neural networks, deep belief networks outperform stacked auto-encoders and multi-layer perceptron neural networks in classification accuracy. The tasks that used deep learning fell into five general groups: emotion recognition, motor imagery, mental workload, seizure detection, event related potential detection, and sleep scoring. For each type of task, we describe the specific input formulation, major characteristics, and end classifier recommendations found through this review. SIGNIFICANCE: This review summarizes the current practices and performance outcomes in the use of deep learning for EEG classification. Practical suggestions on the selection of many hyperparameters are provided in the hope that they will promote or guide the deployment of deep learning to EEG datasets in future research.

Classification and Transfer Learning of EEG during a Kinesthetic Motor Imagery Task using Deep Convolutional Neural Networks.

The reliable classification of Electroencephalography (EEG) signals is a crucial step towards making EEG-controlled non-invasive neuro-exoskeleton rehabilitation a practical reality. EEG signals collected during motor imagery tasks have been proposed to act as a control signal for exoskeleton applications. Here, a Deep Convolutional Neural Network (DCNN) was optimized to classify a two-class kinesthetic motor imagery EEG dataset, leading to an optimized architecture consisting of four convolutional layers and three fully connected layers. Transfer learning, or the leveraging of data from past subjects to classify the intentions of a new subject, is important for rehabilitation as it helps to minimize the number of training sessions required from subjects who lack full motor functionality. The transfer learning training paradigm investigated through this study utilized region criticality trends to reduce the number of new subject training sessions and increase the classification performance when compared against a single-subject non-transfer-learning classifier.