Primitive Shape Imagery Classification from Electroencephalography

Introduction: Brain-computer interfaces (BCIs) augment traditional interfaces for human-computer interaction and provide alternative communication devices to enable the physically impaired to work. Imagined object/shape classification from electroencephalography (EEG) may lead, for example, to enhanced tools for fields such as engineering, design, and the visual arts. Evidence to support such a proposition from non-invasive neuroimaging techniques to date has mainly involved functional magnetic resonance tomography (fMRI) [1] indicating that visual perception and mental imagery show similar brain activity patterns [2] and, although the primary visual cortex has an important role in mental imagery and perception, the occipitotemporal cortex also encodes sensory, semantic and emotional properties during shape imagery [3]. Here we investigate if five imagined primitive shapes (sphere, cone, pyramid, cylinder, cube) can be classified from EEG using filter bank common spatial patterns (FBCSP) [4]. Material, Methods, and Results: Ten healthy volunteers (8 males and 2 females, aged 26-44) participated in a single session study (three runs, four blocks/run, 30 trials/block (i.e., six repetitions of five primitive shapes in random order)). Trials lasted 7s as shown in Fig. 1 and ended with an auditory tone. Thirty EEG channels were recorded with a g.BSamp EEG system using active electrodes (g.tec, Austria). EEG channels with high-level noise were removed. Signals were band-pass filtered in six non-overlapped, 4Hz width bands covering the 4-40Hz frequency range. Filter bank common spatial pattern (FBCSP) based feature extraction and mutual information (MI) based feature selection methods provided input features for 2-class classification using linear discriminant analysis (LDA) for target shape versus the rest, separately. The final 5-class classification was decided by assessing the signed distance in the 2-class discriminant hyperplane for each of the five binary classifiers as shown in Fig. 1. Classifiers were trained on two runs and tested on the one unseen run (i.e., 3 fold cross-validation). A Wilcoxon non-parametric test was used to validate the difference of DA at end of the resting period (-1s) and at the maximal peak accuracy occurring during the shape imagery task (0-3s) is significant (p<0.001). Fig. 1 shows the between-subject average time-varying classification accuracies with standard deviation (shaded area). Discussion: The results indicate that there is separability provided by the shape imagery and there is significantly higher accuracy compared to the ~20% chance level prior the display period with maximum accuracy reaching 34%. In [5] classification of five imagined primitive and complex shapes with 44% accuracy is reported using a 14 channel Emotiv headset. Differences in performance reported may be influenced by EEG recording (EEG in [5] appears to have different dynamics (significant mean shifts)), the study had more sessions/trials, applied ICA for noise removal and the participants had designer experience whilst our study did not. Improvement of our methods is required to achieve higher accuracy rate. It is unclear if an online feedback to shape imagery training and learning will an impact performance – a multisession online study with feedback is the next step in this research. Significance: To best of our knowledge this is only the second study of shape imagery classification from EEG

Workshops of the Sixth International Brain–Computer Interface Meeting: brain–computer interfaces past, present, and future

Brain–computer interfaces (BCI) (also referred to as brain–machine interfaces; BMI) are, by definition, an interface between the human brain and a technological application. Brain activity for interpretation by the BCI can be acquired with either invasive or non-invasive methods. The key point is that the signals that are interpreted come directly from the brain, bypassing sensorimotor output channels that may or may not have impaired function. This paper provides a concise glimpse of the breadth of BCI research and development topics covered by the workshops of the 6th International Brain–Computer Interface Meeting

Electroencephalography and psychological assessment datasets to determine the efficacy of a low-cost, wearable neurotechnology intervention for reducing Post-Traumatic Stress Disorder symptom severity

The datasets described here comprise electroencephalography (EEG) data and psychometric data freely available on data.mendeley.com. The EEG data is available in .mat formatted files containing the EEG signal values structured in two-dimensional (2D) matrices, with channel data and trigger information in rows, and samples in columns (having a sampling rate of 250Hz). Twenty-nine female survivors of the 1994 genocide against the Tutsi in Rwanda, underwent a psychological assessment before and after an intervention aimed at reducing Post-Traumatic Stress Disorder (PTSD) symptom severity. Three measures of trauma and four measures of wellbeing were assessed using empirically validated standardised assessments. The pre- and post- intervention psychometric data were analysed using non-parametric statistical methods and the post-intervention data were further evaluated according to diagnostic assessment rules to determine clinically relevant improvements for each group. The participants were assigned to a control group (CG, n = 9), a motor-imagery group (MI, n = 10), and a neurofeedback group (NF, n = 10). Participants in the latter two groups received Brain-Computer Interface (BCI) based training as a treatment intervention over a sixteen-day period, between the pre- and post- clinical interviews. The training involved presenting feedback visually via a videogame, based on real-time analysis of the EEG recorded data during the BCI-based treatment session. Participants were asked to regulate (NF) or intentionally modulate (MI) brain activity to affect/control the game

In vivo evidence for a novel pathway of vitamin D3 metabolism initiated by P450scc and modified by CYP27B1

We define previously unrecognized in vivo pathways of vitamin D(3) (D3) metabolism generating novel D3-hydroxyderivatives different from 25-hydroxyvitamin D(3) [25(OH)D3] and 1,25(OH)(2)D3. Their novel products include 20-hydroxyvitamin D(3) [20(OH)D3], 22(OH)D3, 20,23(OH)(2)D3, 20,22(OH)(2)D3, 1,20(OH)(2)D3, 1,20,23(OH)(3)D3, and 17,20,23(OH)(3)D3 and were produced by placenta, adrenal glands, and epidermal keratinocytes. We detected the predominant metabolite [20(OH)D3] in human serum with a relative concentration ∼20 times lower than 25(OH)D3. Use of inhibitors and studies performed with isolated mitochondria and purified enzymes demonstrated involvement of the steroidogenic enzyme cytochrome P450scc (CYP11A1) as well as CYP27B1 (1α-hydroxylase). In placenta and adrenal glands with high CYP11A1 expression, the predominant pathway was D3 → 20(OH)D3 → 20,23(OH)(2)D3 → 17,20,23(OH)(3)D3 with further 1α-hydroxylation, and minor pathways were D3 → 25(OH)D3 → 1,25(OH)(2)D3 and D3 → 22(OH)D3 → 20,22(OH)(2)D3. In epidermal keratinocytes, we observed higher proportions of 22(OH)D3 and 20,22(OH)(2)D3. We also detected endogenous production of 20(OH)D3, 22(OH) D3, 20,23(OH)(2)D3, 20,22(OH)(2)D3, and 17,20,23(OH)(3)D3 by immortalized human keratinocytes. Thus, we provide in vivo evidence for novel pathways of D3 metabolism initiated by CYP11A1, with the product profile showing organ/cell type specificity and being modified by CYP27B1 activity. These findings define the pathway intermediates as natural products/endogenous bioregulators and break the current dogma that vitamin D is solely activated through the sequence D3 → 25(OH)D3 → 1,25(OH)(2)D3.—Slominski, A. T., Kim, T.-K., Shehabi, H. Z., Semak, I., Tang, E. K. Y., Nguyen, M. N., Benson, H. A. E., Korik, E., Janjetovic, Z., Chen, J., Yates, C. R., Postlethwaite, A., Li, W., Tuckey, R. C. In vivo evidence for a novel pathway of vitamin D(3) metabolism initiated by P450scc and modified by CYP27B1

DAMTRNN: A Delta attention-based multi-task RNN for intention recognition

Recognizing human intentions from electroencephalographic (EEG) signals is attracting extraordinary attention from the artificial intelligence community because of its promise in providing non-muscular forms of communication and control to those with disabilities. So far, studies have explored correlations between specific segments of an EEG signal and an associated intention. However, there are still challenges to be overcome on the road ahead. Among these, vector representations suffer from the enormous amounts of noise that characterize EEG signals. Identifying the correlations between signals from adjacent sensors on a headset is still difficult. Further, research not yet reached the point where learning models can accept decomposed EEG signals to capture the unique biological significance of the six established frequency bands. In pursuit of a more effective intention recognition method, we developed DAMTRNN, a delta attention-based multi-task recurrent neural network, for human intention recognition. The framework accepts divided EEG signals as inputs, and each frequency range is modeled separately but concurrently with a series of LSTMs. A delta attention network fuses the spatial and temporal interactions across different tasks into high-impact features, which captures correlations over longer time spans and further improves recognition accuracy. Comparative evaluations between DAMTRNN and 14 state-of-the-art methods and baselines show DAMTRNN with a record-setting performance of 98.87% accuracy