I have developed "tennis elbow" from lugging this book around the past four weeks, but it is worth the pain, the effort, and the aspirin. It is also worth the (relatively speaking) bargain price. Including appendixes, this book contains 894 pages of text. The entire panorama of the neural sciences is surveyed and examined, and it is comprehensive in its scope, from genomes to social behaviors. The editors explicitly state that the book is designed as "an introductory text for students of biology, behavior, and medicine," but it is hard to imagine any audience, interested in any fragment of neuroscience at any level of sophistication, that would not enjoy this book. The editors have done a masterful job of weaving together the biologic, the behavioral, and the clinical sciences into a single tapestry in which everyone from the molecular biologist to the practicing psychiatrist can find and appreciate his or

Principles of Neural Science

A brain-computer interface (BCI) is a hardware and software communications system that permits cerebral activity alone to control computers or external devices. The immediate goal of BCI research is to provide communications capabilities to severely disabled people who are totally paralyzed or 'locked in' by neurological neuromuscular disorders, such as amyotrophic lateral sclerosis, brain stem stroke, or spinal cord injury. Here, we review the state-of-the-art of BCIs, looking at the different steps that form a standard BCI: signal acquisition, preprocessing or signal enhancement, feature extraction, classification and the control interface. We discuss their advantages, drawbacks, and latest advances, and we survey the numerous technologies reported in the scientific literature to design each step of a BCI. First, the review examines the neuroimaging modalities used in the signal acquisition step, each of which monitors a different functional brain activity such as electrical, magnetic or metabolic activity. Second, the review discusses different electrophysiological control signals that determine user intentions, which can be detected in brain activity. Third, the review includes some techniques used in the signal enhancement step to deal with the artifacts in the control signals and improve the performance. Fourth, the review studies some mathematic algorithms used in the feature extraction and classification steps which translate the information in the control signals into commands that operate a computer or other device. Finally, the review provides an overview of various BCI applications that control a range of devices.

/pdf/brain-computer-interfaces-a-review-2z42gnpdfy.pdf

Brain Computer Interfaces, a Review

https://biblio.ugent.be/publication/8650534/file/8672654.pdf

Evidence-based guidelines on the therapeutic use of repetitive transcranial magnetic stimulation (rTMS): An update (2014–2018)

Objective. Signal classification is an important issue in brain computer interface (BCI) systems. Deep learning approaches have been used successfully in many recent studies to learn features and classify different types of data. However, the number of studies that employ these approaches on BCI applications is very limited. In this study we aim to use deep learning methods to improve classification performance of EEG motor imagery signals. Approach. In this study we investigate convolutional neural networks (CNN) and stacked autoencoders (SAE) to classify EEG Motor Imagery signals. A new form of input is introduced to combine time, frequency and location information extracted from EEG signal and it is used in CNN having one 1D convolutional and one max-pooling layers. We also proposed a new deep network by combining CNN and SAE. In this network, the features that are extracted in CNN are classified through the deep network SAE. Main results. The classification performance obtained by the proposed method on BCI competition IV dataset 2b in terms of kappa value is 0.547. Our approach yields 9% improvement over the winner algorithm of the competition. Significance. Our results show that deep learning methods provide better classification performance compared to other state of art approaches. These methods can be applied successfully to BCI systems where the amount of data is large due to daily recording.

https://iopscience.iop.org/article/10.1088/1741-2560/14/1/016003/pdf

A novel deep learning approach for classification of EEG motor imagery signals

https://schererstefan.net/assets/files/papers/1-s2.0-S0167639315000369-main.pdf

A review of depression and suicide risk assessment using speech analysis

https://tomatisariston.gr/wp-content/uploads/2014/06/Vestibular-insightsintocognitionandpsychiatry.pdf

Vestibular insights into cognition and psychiatry

Wavelet packets and neural networks have been used to analyze the vibration data of circuit breakers for the detection of incipient circuit breaker faults. Wavelet packets are used to convert measured vibration data from healthy and defective circuit breakers into wavelet features. Selected features highlighting the differences between healthy and faulty condition are processed by a back-propagation neural network for classification. Testing has been done for three 66 kV circuit breakers with simulated faults. Detection accuracy is shown to be far better than other classical techniques such as the windowed Fourier transform, stand alone artificial neural networks or expert system. The accuracy of detection for some faults can be as high as 100%.

New fault diagnosis of circuit breakers

Repetitive transcranial magnetic stimulation (rTMS) uses a magnetic coil to induce an electric field in brain tissue. As a pilot study, we investigated the effect of rTMS treatment on 10 volunteers with Alzheimer’s disease (AD) in a two-stage study. The first stage consisted of a double-blind crossover study with real and sham treatments. Each treatment block consisted of 13 sessions over 4 weeks. During each session, 2000 TMS pulses at 90%–100% of resting motor threshold were applied to dorsolateral prefrontal cortex bilaterally, and the patients were kept cognitively active by object/action naming during the treatment. The second stage was an open-label study, in which the same treatments were performed in 2-week blocks (10 sessions) approximately every 3 months as follow-up treatments on six of the volunteers, who completed the first stage of the study. Primary outcome measures were the Montreal Cognitive Assessment (MOCA) and the Alzheimer’s Disease Assessment Scale-cognitive subscale. The secondary outcome measures were the Revised Memory and Behavior Checklist as well as our team’s custom-designed cognitive assessments. The results showed a noticeably stronger improvement on all assessments during the real treatment as compared to the sham treatment. The changes in MOCA scores as well as our designed cognitive assessment were found to be statistically significant, with particularly strong results in the six volunteers who were in the early stages of the disease. The long-term trends observed in the second stage of the study also showed generally less decline than would be expected for their condition. It appears that rTMS can be an effective tool for improving the cognitive abilities of patients with early to moderate stages of AD. However, the positive effects of rTMS may persist for only up to a few weeks. Specific skills being practiced during rTMS treatment may retain their improvement for longer periods.

https://journals.sagepub.com/doi/pdf/10.4137/JEN.S24004

Short and Long-term Effects of rTMS Treatment on Alzheimer's Disease at Different Stages: A Pilot Study.

Wavelet Common Spatial Pattern in asynchronous offline brain computer interfaces

Asymmetry in brain activity has been hypothesised to be a potential marker for depression. However, it is not yet fully understood nor is it reliable enough to be in regular clinical use. In this study frontal electroencephalograph (EEG) recordings were taken from 21 subjects, from which frontal brain asymmetry (FBA) ratios were calculated. The results were classified according to whether or not the subject had a recent history of depression, and by the presence or absence of a characteristic peak in the `alpha' region of the EEG spectrum. It was found that subjects with current or previous incidence of depressive disorders tend to have an FBA ratio that lies towards the extremities of the distribution, and that the presence of a `clear alpha peak' made the assessment process more reliable

Brian J Lithgow

Papers

Vestibular insights into cognition and psychiatry

New fault diagnosis of circuit breakers

Short and Long-term Effects of rTMS Treatment on Alzheimer's Disease at Different Stages: A Pilot Study.

Wavelet Common Spatial Pattern in asynchronous offline brain computer interfaces

Alpha-band characteristics in EEG spectrum indicate reliability of frontal brain asymmetry measures in diagnosis of depression.