Deep Learning based Inter-subject Continuous Decoding of Motor Imagery for Practical Brain-Computer Interfaces

Inter-subject transfer learning is a long-standing problem in brain-computer interfaces (BCIs) and has not yet been fully realized due to high inter-subject variability in the brain signals related to motor imagery (MI). The recent success of deep learning-based algorithms in classifying different brain signals warrants further exploration to determine whether it is feasible for the inter-subject continuous decoding of MI signals to provide contingent neurofeedback which is important for neurorehabilitative BCI designs. In this paper, we have shown how a convolutional neural network (CNN) based deep learning framework can be used for inter-subject continuous decoding of MI related electroencephalographic (EEG) signals using the novel concept of Mega Blocks for adapting the network against inter-subject variabilities. These Mega Blocks have the capacity to repeat a specific architectural block several times such as one or more convolutional layers in a single Mega Block. The parameters of such Mega Blocks can be optimized using Bayesian hyperparameter optimization. The results, obtained on the publicly available BCI competition IV-2b dataset, yields an average inter-subject continuous decoding accuracy of 71.49% (kappa=0.42) and 70.84% (kappa =0.42) for two different training methods such as adaptive moment estimation (Adam) and stochastic gradient descent (SGDM) respectively in 7 out of 9 subjects. Our results show for the first time that it is feasible to use CNN based architectures for inter-subject continuous decoding with a sufficient level of accuracy for developing calibration-free MI-BCIs for practical purposes

Assessing impact of channel selection on decoding of motor and cognitive imagery from MEG data

Objective: Magnetoencephalography (MEG) based Brain-Computer Interface (BCI) involves a large number of sensors allowing better spatiotemporal resolution for assessing brain activity patterns. There have been many efforts to develop BCI using MEG with high accuracy, though an increase in the number of channels means an increase in computational complexity. However, not all sensors necessarily contribute significantly to an increase in classification accuracy, and specifically in the case of MEG-based BCI no channel selection methodology has been performed. Therefore, this study investigates the effect of channel selection on the performance of MEG-based BCI. Approach: MEG data were recorded for two sessions from 15 healthy participants performing motor imagery, cognitive imagery and a mixed imagery task pair using a unique paradigm. Performance of four state-of-the-art channel selection methods (i.e. Class-Correlation (CC), ReliefF (RF), Random Forest (RandF), and Infinite Latent Feature Selection (ILFS) were applied across six binary tasks in three different frequency bands) was evaluated in this study on two state-of-the-art features i.e. bandpower and CSP. Main results: All four methods provided a statistically significant increase in classification accuracy (CA) compared to a baseline method using all gradiometer sensors, i.e. 204 channels with band-power features from alpha (8-12Hz), beta (13-30Hz), or broadband (alpha+beta ) (8-30Hz). It is also observed that the alpha frequency band performed better than the beta and broadband frequency bands. The performance of the beta band gave the lowest CA compared with the other two bands. Channel selection improved accuracy irrespective of feature types. Moreover, all the methods reduced the number of channels significantly, from 204 to a range of 1-25, using bandpower as a feature and from 15-105 for CSP. The optimal channel number also varied not only in each session but also for each participant. Reducing the number of channels will help to decrease the computation cost and maintain numerical stability in cases of low trial numbers. Significance: The study showed significant improvement in performance of MEG-BCI with channel selection irrespective of feature type and hence can be successfully applied for BCI applications

Mapping & decoding cortical engagement during motor imagery, mental arithmetic, and silent word generation using MEG

Accurate quantification of cortical engagement during mental imagery tasks remains a challenging brain-imaging problem with immediate relevance to developing brain–computer interfaces. We analyzed magnetoencephalography (MEG) data from 18 individuals completing cued motor imagery, mental arithmetic, and silent word generation tasks. Participants imagined movements of both hands (HANDS) and both feet (FEET), subtracted two numbers (SUB), and silently generated words (WORD). The task-related cortical engagement was inferred from beta band (17–25 Hz) power decrements estimated using a frequency-resolved beamforming method. In the hands and feet motor imagery tasks, beta power consistently decreased in premotor and motor areas. In the word and subtraction tasks, beta-power decrements showed engagements in language and arithmetic processing within the temporal, parietal, and inferior frontal regions. A support vector machine classification of beta power decrements yielded high accuracy rates of 74 and 68% for classifying motor-imagery (HANDS vs. FEET) and cognitive (WORD vs. SUB) tasks, respectively. From the motor-versus-nonmotor contrasts, excellent accuracy rates of 85 and 80% were observed for hands-versus-word and hands-versus-sub, respectively. A multivariate Gaussian-process classifier provided an accuracy rate of 60% for the four-way (HANDS-FEET-WORD-SUB) classification problem. Individual task performance was revealed by within-subject correlations of beta-decrements. Beta-power decrements are helpful metrics for mapping and decoding cortical engagement during mental processes in the absence of sensory stimuli or overt behavioral outputs. Markers derived based on beta decrements may be suitable for rehabilitation purposes, to characterize motor or cognitive impairments, or to treat patients recovering from a cerebral stroke

Challenges in Blockchain as a Solution for IoT Ecosystem Threats and Access Control: A Survey

The Internet of Things (IoT) is increasingly influencing and transforming various aspects of our daily lives. Contrary to popular belief, it raises security and privacy issues as it is used to collect data from consumers or automated systems. Numerous articles are published that discuss issues like centralised control systems and potential alternatives like integration with blockchain. Although a few recent surveys focused on the challenges and solutions facing the IoT ecosystem, most of them did not concentrate on the threats, difficulties, or blockchain-based solutions. Additionally, none of them focused on blockchain and IoT integration challenges and attacks. In the context of the IoT ecosystem, overall security measures are very important to understand the overall challenges. This article summarises difficulties that have been outlined in numerous recent articles and articulates various attacks and security challenges in a variety of approaches, including blockchain-based solutions and so on. More clearly, this contribution consolidates threats, access control issues, and remedies in brief. In addition, this research has listed some attacks on public blockchain protocols with some real-life examples that can guide researchers in taking preventive measures for IoT use cases. Finally, a future research direction concludes the research gaps by analysing contemporary research contributions

Experiences of a 'screen and treat' cervical cancer prevention programme among brothel-based female sex workers in Bangladesh: A qualitative interview study

OBJECTIVES: Little is known about sex workers' experiences of cervical cryotherapy. We sought to understand sex workers' perspectives of 'screen and treat' programmes and their management of the World Health Organization post-treatment guidance to abstain from sex or use condoms consistently for 4 weeks. We explored contraceptive preferences and use of menstrual regulation services. METHODS: We conducted semi-structured interviews with 16 sex workers and six brothel leaders in an urban brothel complex in Bangladesh between October and November 2018. All had undergone cryotherapy. We conducted a thematic analysis using deductive coding, informed by a priori themes, and inductive data-driven coding. RESULTS: Most sex workers could not abstain from sex during the healing period. Consistent condom use was challenging due to economic incentives attached to condomless sex and coercive behaviours of clients. The implications of non-adherence among high-risk groups such as sex workers are not known. Use of short-acting methods of contraception was common, and discontinuation was high due to side effects and other perceived health concerns. The majority of sex workers and brothel leaders had utilized menstrual regulation services. Barriers to accessing timely menstrual regulation and other sexual and reproductive health services included limited mobility, economic costs, and discriminatory attitudes of health care workers. CONCLUSION: Service innovations are required to enable sex workers to abstain or use condoms consistently in the post-cryotherapy healing phase and to address sex workers' broader sexual and reproductive health needs. Further research is required to assess the risk of HIV and other sexually transmitted infection transmission following cryotherapy among high-risk groups

Reducing the environmental impact of surgery on a global scale: systematic review and co-prioritization with healthcare workers in 132 countries

Abstract Background Healthcare cannot achieve net-zero carbon without addressing operating theatres. The aim of this study was to prioritize feasible interventions to reduce the environmental impact of operating theatres. Methods This study adopted a four-phase Delphi consensus co-prioritization methodology. In phase 1, a systematic review of published interventions and global consultation of perioperative healthcare professionals were used to longlist interventions. In phase 2, iterative thematic analysis consolidated comparable interventions into a shortlist. In phase 3, the shortlist was co-prioritized based on patient and clinician views on acceptability, feasibility, and safety. In phase 4, ranked lists of interventions were presented by their relevance to high-income countries and low–middle-income countries. Results In phase 1, 43 interventions were identified, which had low uptake in practice according to 3042 professionals globally. In phase 2, a shortlist of 15 intervention domains was generated. In phase 3, interventions were deemed acceptable for more than 90 per cent of patients except for reducing general anaesthesia (84 per cent) and re-sterilization of ‘single-use’ consumables (86 per cent). In phase 4, the top three shortlisted interventions for high-income countries were: introducing recycling; reducing use of anaesthetic gases; and appropriate clinical waste processing. In phase 4, the top three shortlisted interventions for low–middle-income countries were: introducing reusable surgical devices; reducing use of consumables; and reducing the use of general anaesthesia. Conclusion This is a step toward environmentally sustainable operating environments with actionable interventions applicable to both high– and low–middle–income countries

Cobalt assisted cleavage of S---S bonds and a base-free synthesis of mercapturic acids

Base free transformation of PhSSPh to sulfides, PhSR (R= alkyl, benzyl, allyl, acyl) and N-acetyl-L-cystine to mercapturic acids [AcNHCH(COOH)CH<SUB>2</SUB>SR, R=alkyl, benzyl, allyl, acyl] have been achieved using Zn/cat. CoCl<SUB>2</SUB>/organic halide in MeCN at room temperature

The first example of a catalytic Hunsdiecker reaction: synthesis of β-halostyrenes

This article does not have an abstract

Manganese (II) catalysed Hunsdiecker reaction: a facile entry to α-(dibromomethyl)benzenemethanol

Manganese (II) acetate catalysed reactions of α,β-unsaturated aromatic carboxylic acids 1 with NBS (1 and 2 eq.) in MeCN/water afford haloalkenes 2 and α-(dibromomethyl)benzenemethanols 3 respectively

Is metal necessary in the Hunsdiecker-Borodin reaction?

The tetrabutylammonium trifluoroacetate (TBATFA) catalyzed conversion of α,β-unsaturated carboxylic acids to the corresponding halides with N-halosuccinimides in dichloroethane is reported as the first example of a metal-free catalytic version of the title reaction. The methodology was further employed for a facile synthesis of piperine