Convolutional neural networks (CNNs) have recently been applied to electroencephalogram (EEG)-based brain-computer interfaces (BCIs). EEG is a noninvasive neuroimaging technique, which can be used to decode user intentions. Because the feature space of EEG data is highly dimensional and signal patterns are specific to the subject, appropriate methods for feature representation are required to enhance the decoding accuracy of the CNN model. Furthermore, neural changes exhibit high variability between sessions, subjects within a single session, and trials within a single subject, resulting in major issues during the modeling stage. In addition, there are many subject-dependent factors, such as frequency ranges, time intervals, and spatial locations at which the signal occurs, which prevent the derivation of a robust model that can achieve the parameterization of these factors for a wide range of subjects. However, previous studies did not attempt to preserve the multivariate structure and dependencies of the feature space. In this study, we propose a method to generate a spatiospectral feature representation that can preserve the multivariate information of EEG data. Specifically, 3-D feature maps were constructed by combining subject-optimized and subject-independent spectral filters and by stacking the filtered data into tensors. In addition, a layer-wise decomposition model was implemented using our 3-D-CNN framework to secure reliable classification results on a single-trial basis. The average accuracies of the proposed model were 87.15% (±7.31), 75.85% (±12.80), and 70.37% (±17.09) for the BCI competition data sets IV_2a, IV_2b, and OpenBMI data, respectively. These results are better than those obtained by state-of-the-art techniques, and the decomposition model obtained the relevance scores for neurophysiologically plausible electrode channels and frequency domains, confirming the validity of the proposed approach.

This paper evaluates the possibility of performing fine gesture recognition including finger movements on a low-tech device. In particular, we present a solution with a recognition model that is small enough to fit in the memory of a low- tech device and describe related difficulties associated with this approach. Several different Machine Learning techniques are employed and their individual advantages and drawbacks are explored for the task at hand. Our results indicate an average of 95% accuracy during real-time testing for an eight class decoding task with a custom Recurrent Neural Network approach, that runs on the low-tech device, namely an Arduino Nano 33 BLE. The novelty and strength of this research lies in the fact that we are able to recognize fine hand gestures including finger movements rather than recognizing only coarse hand gestures. The recognition process is conducted on the low-tech device and as a result this solution has all advantages that are typically associated with embedded systems, namely cost-efficiency, battery life efficiency, and a high degree of independence from other devices as well as compatibility with them.

Recently, there has been an increased demand for Brain-Machine Interface (BMI) toolboxes for neuroscientifc research. In many BMI applications, speller systems can provide an efficient communication channel for users with disabilities. Here, we introduce an open-source BMI toolbox termed `OpenBMI', which supports the various signal processing chains for common BMI paradigms, such as event-related potentials (ERPs) and steady-state visual evoked potentials (SSVEP). The OpenBMI framework consists of ready-to-use experimental paradigms, offline data analysis techniques, online feedback as well as evaluation modules. The data analysis modules provide essential pre-processing steps (segmentation, baseline correction, etc.) as well as signal processing algorithms such as temporal and spatial filtering, artifact rejection, among others. The experimental paradigms of ERP and SSVEP are available with fully open-sourced demo scripts. Users can easily modify or extend the demo scripts for their needs. In this article, the OpenBMI framework, its features as well as its future development plan is introduced.

Lately, electroencephalography (EEG)-based auth- entication has received considerable attention from the scientific community. However, the limited usability of wet EEG electrodes as well as low accuracy for large numbers of users have so far prevented this new technology to become commonplace. In this study a novel EEG-based authentication system is presented, which is based on the rapid serial visual presentation paradigm and uses a knowledge-based approach for authentication. Twenty-nine subjects’ data were recorded and analyzed with wet EEG electrodes as well as dry ones. A true acceptance rate of 100% can be reached for all subjects with an average required login time of 13.5 s for wet and 27 s for dry electrodes. Average false acceptance rates for the dry electrode setup were estimated to be$3.33 \times 10^{-5}$.

Multimodal data are ubiquitous in engineering, communications, robotics, computer vision, or more generally speaking in industry and the sciences. All disciplines have developed their respective sets of analytic tools to fuse the information that is available in all measured modalities. In this paper, we provide a review of classical as well as recent machine learning methods (specifically factor models) for fusing information from functional neuroimaging techniques such as: LFP, EEG, MEG, fNIRS, and fMRI. Early and late fusion scenarios are distinguished, and appropriate factor models for the respective scenarios are presented along with example applications from selected multimodal neuroimaging studies. Further emphasis is given to the interpretability of the resulting model parameters, in particular by highlighting how factor models relate to physical models needed for source localization. The methods we discuss allow for the extraction of information from neural data, which ultimately contributes to 1) better neuroscientific understanding; 2) enhance diagnostic performance; and 3) discover neural signals of interest that correlate maximally with a given cognitive paradigm. While we clearly study the multimodal functional neuroimaging challenge, the discussed machine learning techniques have a wide applicability, i.e., in general data fusion, and may thus be informative to the general interested reader.

Brain-computer interfaces (BCIs) are successfully used in scientific, therapeutic and other applications. Remaining challenges are among others a low signal-to-noise ratio of neural signals, lack of robustness for decoders in the presence of inter-trial and inter-subject variability, time constraints on the calibration phase and the use of BCIs outside a controlled lab environment. Recent advances in BCI research addressed these issues by novel combinations of complementary analysis as well as recording techniques, so called hybrid BCIs. In this paper, we review a number of data fusion techniques for BCI along with hybrid methods for BCI that have recently emerged. Our focus will be on sensorimotor rhythm-based BCIs. We will give an overview of the three main lines of research in this area, integration of complementary features of neural activation, integration of multiple previous sessions and of multiple subjects, and show how these techniques can be used to enhance modern BCI systems.

The urge to further our understanding of multimodal neural data has recently become an important topic due to the ever increasing availability of simultaneously recorded data from different neural imaging modalities. In case where EEG is one of the modalities, it is of interest to relate a nonlinear function of the raw EEG time-domain signal, say, EEG band power, to another modality such as the hemodynamic response, as measured with NIRS or fMRI. In this work we tackle exactly this problem defining a novel algorithm that we denote multimodal source power correlation analysis (mSPoC). The validity and high performance of the mSPoC framework is demonstrated for simulated and real-world multimodal data.

The accurate prediction of molecular energetics in chemical compound space is a crucial ingredient for rational compound design. The inherently graph-like, non-vectorial nature of molecular data gives rise to a unique and difficult machine learning problem. In this paper, we adopt a learning-from-scratch approach where quantum-mechanical molecular energies are predicted directly from the raw molecular geometry. The study suggests a benefit from setting flexible priors and enforcing invariance stochastically rather than structurally. Our results improve the state-of-the-art by a factor of almost three, bringing statistical methods one step closer to the holy grail of ''chemical accuracy''.

In the quest to make Brain Computer Interfacing (BCI) more usable, dry electrodes have emerged that get rid of the initial 30 minutes required for placing an electrode cap. Another time consuming step is the required individualized adaptation to the BCI user, which involves another 30 minutes calibration for assessing a subjects brain signature. In this paper we aim to also remove this calibration proceedure from BCI setup time by means of machine learning. In particular, we harvest a large database of EEG BCI motor imagination recordings (83 subjects) for constructing a library of subject-specific spatio-temporal filters and derive a subject independent BCI classifier. Our offline results indicate that BCI-na{i}ve users could start real-time BCI use with no prior calibration at only a very moderate performance loss."