On the Relative Contribution of Deep Convolutional Neural Networks for SSVEP-based Bio-Signal Decoding in BCI Speller Applications

Brain-computer interfaces (BCI) harnessing Steady State Visual Evoked Potentials (SSVEP) manipulate the frequency and phase of visual stimuli to generate predictable oscillations in neural activity. For BCI spellers, oscillations are matched with alphanumeric characters allowing users to select target numbers and letters. Advances in BCI spellers can, in part, be accredited to subject-speci?c optimization, including; 1) custom electrode arrangements, 2) ?lter sub-band assessments and 3) stimulus parameter tuning. Here we apply deep convolutional neural networks (DCNN) demonstrating cross-subject functionality for the classi?cation of frequency and phase encoded SSVEP. Electroencephalogram (EEG) data are collected and classi?ed using the same parameters across subjects. Subjects ?xate forty randomly cued ?ickering characters (5 ×8 keyboard array) during concurrent wet-EEG acquisition. These data are provided by an open source SSVEP dataset. Our proposed DCNN, PodNet, achieves 86% and 77% of?ine Accuracy of Classi?cation across-subjects for two data capture periods, respectively, 6-seconds (information transfer rate= 40bpm) and 2-seconds (information transfer rate= 101bpm). Subjects demonstrating sub-optimal (< 70%) performance are classi?ed to similar levels after a short subject-speci?c training period. PodNet outperforms ?lter-bank canonical correlation analysis (FBCCA) for a low volume (3channel) clinically feasible occipital electrode con?guration. The networks de?ned in this study achieve functional performance for the largest number of SSVEP classes decoded via DCNN to date. Our results demonstrate PodNet achieves cross-subject, calibrationless classi?cation and adaptability to sub-optimal subject data and low-volume EEG electrode arrangements

Evaluating the specialist palliative care clinical nurse specialist role in an acute hospital setting: a mixed methods sequential explanatory study.

Special palliative care is provided in a range of settings including a patient's home (their primary place of dwelling), a hospice in-patient unit, or an acute hospital. The aim of the study was to evaluate the role of the specialist in palliative care clinical nurse specialist (SPC CNS) role in an acute hospital setting. This study was conducted using a mixed methods sequential explanatory approach in two phases; phase 1 involved completion of a study questionnaire (n = 121) and phase 2 involved part-taking in a focus group (n = 6) or individual interview (n = 4). Phase 1 results indicated that respondents held positive attitudes towards the Specialist Palliative Care Clinical Nurses Specialist (SPC CNS) in relation to clinical care, education and patient advocacy. Phase 2 qualitative findings identified the importance of the role in terms of symptom management, education and support. This study provides an evaluation of a SPC CNS role since it was established in an acute hospital setting. The evidence indicates that there is a varied understanding of the role of the SPC CNS. The role was seen as an important one particularly in terms of referrals to and support provided by the SPC CNS, as well as recognition of the importance of the role is providing ongoing education to staff

The People Question: Creating Global Advantage through Global Talent Initiatives

As agribusiness has evolved from a local, generalized activity into a global, specialized industry, fewer of the stakeholders, from producers to consumers, really understand the food production system. Education is the key to navigating the challenges of global agribusiness

Using Variable Natural Environment Brain-Computer Interface Stimuli for Real-time Humanoid Robot Navigation

This paper addresses the challenge of humanoid robot teleoperation in a natural indoor environment via a Brain-Computer Interface (BCI). We leverage deep Convolutional Neural Network (CNN) based image and signal understanding to facilitate both real-time object detection and dry-Electroencephalography (EEG) based human cortical brain bio-signals decoding. We employ recent advances in dry-EEG technology to stream and collect the cortical waveforms from subjects while they fixate on variable Steady State Visual Evoked Potential (SSVEP) stimuli generated directly from the environment the robot is navigating. To these ends, we propose the use of novel variable BCI stimuli by utilising the real-time video streamed via the on-board robot camera as visual input for SSVEP, where the CNN detected natural scene objects are altered and flickered with differing frequencies (10Hz, 12Hz and 15Hz). These stimuli are not akin to traditional stimuli - as both the dimensions of the flicker regions and their on-screen position changes depending on the scene objects detected. Onscreen object selection via such a dry-EEG enabled SSVEP methodology, facilitates the on-line decoding of human cortical brain signals, via a specialised secondary CNN, directly into teleoperation robot commands (approach object, move in a specific direction: right, left or back). This SSVEP decoding model is trained via a priori offline experimental data in which very similar visual input is present for all subjects. The resulting classification demonstrates high performance with mean accuracy of 85% for the real-time robot navigation experiment across multiple test subjects

On the Classification of SSVEP-Based Dry-EEG Signals via Convolutional Neural Networks

Electroencephalography (EEG) is a common signal acquisition approach employed for Brain-Computer Interface (BCI) research. Nevertheless, the majority of EEG acquisition devices rely on the cumbersome application of conductive gel (so-called wet-EEG) to ensure a high quality signal is obtained. However, this process is unpleasant for the experimental participants and thus limits the practical application of BCI. In this work, we explore the use of a commercially available dry-EEG headset to obtain visual cortical ensemble signals. Whilst improving the usability of EEG within the BCI context, dry-EEG suffers from inherently reduced signal quality due to the lack of conduit gel, making the classification of such signals significantly more challenging. In this paper, we propose a novel Convolutional Neural Network (CNN) approach for the classification of raw dry-EEG signals without any data pre-processing. To illustrate the effectiveness of our approach, we utilise the Steady State Visual Evoked Potential (SSVEP) paradigm as our use case. SSVEP can be utilised to allow people with severe physical disabilities such as Complete Locked-In Syndrome or Amyotrophic Lateral Sclerosis to be aided via BCI applications, as it requires only the subject to fixate upon the sensory stimuli of interest. Here we utilise SSVEP flicker frequencies between 10 to 30 Hz, which we record as subject cortical waveforms via the dry-EEG headset. Our proposed end-to-end CNN allows us to automatically and accurately classify SSVEP stimulation directly from the dry-EEG waveforms. Our CNN architecture utilises a common SSVEP Convolutional Unit (SCU), comprising of a 1D convolutional layer, batch normalization and max pooling. Furthermore, We compare several deep learning neural network variants with our primary CNN architecture, in addition to traditional machine learning classification approaches. Experimental evaluation shows our CNN architecture to be significantly better than competing approaches, achieving a classification accuracy of 96% whilst demonstrating superior cross-subject performance and even being able to generalise well to unseen subjects whose data is entirely absent from the training process

Leveraging Synthetic Subject Invariant EEG Signals for Zero Calibration BCI

Recently, substantial progress has been made in the area of Brain-Computer Interface (BCI) using modern machine learning techniques to decode and interpret brain signals. While Electroencephalography (EEG) has provided a non-invasive method of interfacing with a human brain, the acquired data is often heavily subject and session dependent. This makes the seamless incorporation of such data into realworld applications intractable as the subject and session data variance can lead to long and tedious calibration requirements and cross-subject generalisation issues. Focusing on a Steady State Visual Evoked Potential (SSVEP) classification systems, we propose a novel means of generating highly-realistic synthetic EEG data invariant to any subject, session or other environmental conditions. Our approach, entitled the Subject Invariant SSVEP Generative Adversarial Network (SIS-GAN), produces synthetic EEG data from multiple SSVEP classes using a single network. Additionally, by taking advantage of a fixed-weight pre-trained subject classification network, we ensure that our generative model remains agnostic to subject-specific features and thus produces subject-invariant data that can be applied to new previously unseen subjects. Our extensive experimental evaluation demonstrates the efficacy of our synthetic data, leading to superior performance, with improvements of up to 16 percentage points in zero-calibration classification tasks when trained using our subject-invariant synthetic EEG signals

Maternal and peer regulation of adolescent emotion: Associations with depressive symptoms

Contains fulltext : 158669.pdf (publisher's version ) (Closed access)Emotion socialization by close relationship partners plays a role in adolescent depression. In the current study, a microsocial approach was used to examine how adolescents' emotions are socialized by their mothers and close friends in real time, and how these interpersonal emotion dynamics are related to adolescent depressive symptoms. Participants were 83 adolescents aged 16 to 17 years who participated in conflict discussions with their mothers and self-nominated close friends. Adolescents' positive and negative emotions, and mothers' and peers' supportive regulation of adolescent emotions, were coded in real time. Two multilevel survival analyses in a 2-level Cox hazard regression framework predicted the hazard rate of (1) mothers' supportive regulation of adolescents' emotions, and (2) peers' supportive regulation of adolescents' emotions. The likelihood of maternal supportiveness, regardless of adolescent emotions, was lower for adolescents with higher depressive symptoms. In addition, peers were less likely to up-regulate adolescent positive emotions at higher levels of adolescent depressive symptoms. The results of the current study support interpersonal models of depression and demonstrate the importance of real-time interpersonal emotion processes in adolescent depressive symptoms.12 p