The effects of mental training on brain computer interface performance with distractions

The overall success of a brain computer interface (BCI) is largely dependent on the features used to make decisions. Noise in the electroencephalography (EEG) increases the difficulty of acquiring meaningful features. Previous literature suggests teaching subjects meditation and relaxation techniques may improve features relevant to BCI operation. The purpose of this study was to investigate performance on several cognitive protocols for both individuals who use meditation techniques and those who do not use these techniques. Both groups were given a motor imagery based BCI protocol, a P300 speller BCI, a verbal learning task, and an N-back test. No significant difference in performance was found between meditation and control groups. Our research does suggest however, significant differences for the P300 and motor imagery protocols may be found if a larger group (\u3e20 subjects per class) is recruited

Un análisis funcional de dos pistolas antiguas escaneadas en 3D de Nueva Zelanda

[EN] Preservation of historical weapons requirescontinual andcareful maintenance. Digital three-dimensional (3D) scanning can assist in preservation and analysis by generating a 3D computer model. New Zealand presents a specialcase for historical preservation, owing to the rapid import of European goods in a culture previously unexposed to metalworking. This, and the subsequent British colonization, led to upheaval and war.The most intense conflict between British and Maori forces was in the New Zealand Land Wars of the mid-19thcentury.The primary handheld firearms used in this period were black-powder muzzle-loaders, and the varietyof armed factions involved in the war resulted in an eclectic range of weapons used.Two antique muzzle-loading pistols from this period were scanned and analyzed. Insights were gained into the historyof double-barreledmuzzle-loading pistolsand transitional revolvers. The double-barreledpistol was determined to have been a flintlock pistol from a century prior to the Land Wars, later converted to percussion cap ignition. The transitional revolver was an intermediate step between the multi-barrel pepperbox pistol and the “true” revolver, but it remained in use throughout the Victorian era. Both types of firearms were effectively obsolete elsewhere in the world by the time of the Land Wars,but the conflict created a demand for a variety of weapons.While the pistols analyzed in this study are decommissioned and no longer in working order, the 3D models made from the samplesafforded a unique glimpse into New Zealand’shistory. The methodology detailed over the course of the studycan be applied to other historical firearmsin order to facilitate preservation, investigation, and experimentation.[ES] La preservación de las armas históricas requiere un mantenimiento continuo y cuidadoso. El escaneo digital tridimensional (3D) puede ayudar en la preservación y el análisis mediante la generación de un modelo informático en 3D. Nueva Zelanda presenta un caso especial para la preservación histórica, debido a la rápida importación de productos europeos en una cultura que no estaba expuesta a la metalurgia. Esto, junto con la posterior colonización británica, provocó disturbios y guerras. El conflicto más intenso entre las fuerzas británicas y maoríes se produjo en la Guerra delas Tierrasde Nueva Zelanda amediados del siglo XIX. Las principales armas de fuego de mano utilizadas en este período fueron los cargadores de pólvora negra, y la variedad de facciones armadas involucradas en la guerra dio lugar a una gama ecléctica de armas utilizadas. Dos antiguas pistolas de carga con boca de este período fueron escaneadas y analizadas. Se adquirieron conocimientos sobre la historia de las pistolas de doble cañón de carga con boca y los revólveres de transición. Se determinó que la pistola de doble cañón era una pistola de chispa de un siglo antes de la Guerra de lasTierras, convertida más tarde en una pistola de percusión. El revólver de transición fue un paso intermedio entre el revolver pimenterode varios cañones y el "verdadero" revólver, pero se siguió utilizando durante toda la época victoriana. Ambos tipos de armas de fuego habían quedadoobsoletas en otras partes del mundo en el momento de las Guerras, pero el conflicto creó una demanda de variedad de armas. Aunque las pistolas analizadas en este estudio están fuera de servicio y ya no funcionan, los modelos en 3D realizados a partir de las muestras permitieron dar un vistazo único a la historia de Nueva Zelanda. 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Detection of microsleeps from the eeg via optimized classification techniques.

Microsleeps are complete breaks in responsiveness for 0.5–15 s. They can lead to multiple fatalities in certain occupational fields (e.g., transportation and military) due to the need in such occupations for extended and continuous vigilance. Therefore, an automated microsleep detection system may assist in the reduction of poor job performance and occupational fatalities. An EEG-based microsleep detector offers advantages over a videobased microsleep detector, including speed and temporal resolution. A series of software modules were implemented to examine different feature sets to determine the optimal circumstances for automated EEG-based microsleep detection. The microsleep detection system was organized in a similar manner to an EEG-based brain-computer interface (BCI). EEG data underwent baseline removal and filtering to remove overhead noise. Following this, feature extraction generated spectral features based upon an estimate of the power spectrum or its logarithmic transform. Following this, feature selection/reduction (FS/R) was used to select the most relevant information across all the spectral features. A trained classifier was then tested on data from a subject it had not seen before. In certain cases, an ensemble of classifiers was used instead of a single classifier. The performance measures from all cases were then averaged together in leave-one-out crossvalidation (LOOCV). Sets of artificial data were generated to test a prototype EEG-based microsleep detection system, consisting of a combination of EEG and 2-s bursts of 15 Hz sinusoids of varied signal-to-noise ratios (SNRs) ranging from 16 down to 0.03. The balance between events and non-events was varied between evenly balanced and highly imbalanced (e.g., events occurring only 2% of the time). Features were spectral estimates of various EEG bands (e.g., alpha band power) or ratios between them. A total of 34 features for each of the 16 channels yielded a total of 544 features. Five minutes of EEG from eight subjects were used in the generation of the dummy data, and each subject yielded a matrix of 300 observations of 544 features. Datasets from two prior microsleep studies were employed after validating the system on the artificial data. The first, Study A (N = 8), had 16 channels sampled at 256 Hz from two 1-hour sessions per subject and the second, Study C (N = 10), had one 50-min session with 30-62 channels per subject sampled at 250 Hz. A vector of 34 spectral features from each channel was concatenated into a feature vector for each 2-s interval, with each interval having a 1-s overlap with the prior one. In both cases, microsleeps had been identified via a combination of video recording and performance on a continuous tracking task. Study A provided four datasets to compare effects of various preprocessing techniques on performance: (1) Study A bipolar EEG with Independent Component Analysis (ICA) preprocessing and artefact pruning (total automated rejection of artefact-containing epochs) and logarithmic transforms of the spectral features (SABIL); (2) Study A bipolar EEG with ICA-based eye blink removal and artefact removal with pruning of epochs with major artefacts, and linear spectral features (SABIS); (3) Study A referential EEG unprocessed by ICA with spectral features (SARUS); and (4) Study A bipolar EEG unprocessed by ICA with spectral features (SABUS). The second study had one primary feature set, the Study C referential EEG ICA preprocessed spectral feature (SCRIS) variant. LOOCV was evaluated based on the phi correlation coefficient. After replicating prior work, several FS/R and classifier structures were investigated with both the artificially balanced and unbalanced data. Feature selection/reduction methods included principal component analysis (PCA), common spatial patterns (CSP), projection to latent structures (PLS), a new method based on average distance between events and nonevents (ADEN), ADEN normalized with a z-score transform (ADENZ), genetic algorithms in concert with ADEN (GADEN), and genetic algorithms in concert with ADENZ (GADENZ). Several pattern recognition algorithms were investigated: linear discriminant analysis (LDA), radial basis functions (RBFs), and Support Vector Machines with Gaussian (SVMG) and polynomial (SVMP) kernels. Classifier structures examined included single classifiers, bagging, boosting, stacking, and adaptive boosting (AdaBoost). The highest LOOCV results on artificial data (SNR = 0.3) corresponded to GADEN with 10 features and a single LDA classifier with a mean phi value of 0.96. Of the four Study A datasets, PCA with 150 features and a stacking ensemble achieved the highest mean phi of 0.40 with the SABIL feature set, and ADEN with 20 features with a single LDA classifier achieved the highest mean phi of 0.10 with Study C. Other machine-learning methodologies, such as training on artificially balanced data, decreasing the training size, within-subject training and testing, and randomly mixed data from across subjects, were also examined. Training on artificially balanced data did not improve performance. An issue found by performing within-subject training and testing was that, for certain subjects, a classifier trained on one-half of the subject’s data and then tested on the other half was that classifier performance dropped to random guessing. The low phi values on within-subject tests occurred independently of the feature selection/reduction method explored. As such, performance of a standard LOOCV was often dependent on whether a particular testing subject had a low (< 0.15) within-subjects mean phi correlation coefficient. Training on only the higher mean phi values did not boost performance. Additional tests found correlations (r = 0.57, p = 0.003 for Study A and r = 0.67, p 0.15) and longer mean microsleep durations. Other individual subject characteristics, such as number of microsleeps and subject age, did not have significant differences. The primary findings highlighted the strengths and limitations of supervised feature selection and linear classifiers trained upon highly variable between-subject features across two studies. Findings suggested that a classifier performs best when individuals have high mean microsleep durations. On the configurations investigated, preprocessing factors, such as ICA preprocessing, feature extraction method, and artefact pruning, affected the performance more than changing specific module configurations. No significant differences between the SABIL features and the lower performing Study A feature sets were found due to overlapping ranges of performance (p = 0.15). The findings suggest that the investigated techniques plateaued in performance on the Study A data, reaching a point of diminishing returns without fundamentally changing the nature of the classification problem. The different number of channels of varying quality across all subjects in Study C rendered microsleep classification extremely difficult, but even a linear classifier can properly generalize if exposed to a large enough variety of data from across the entire set. Many of the techniques explored are also relevant to other fields, such as braincomputer interface (BCI) and machine learning

Evaluation of an English language phoneme-based imagined speech brain computer interface with low-cost electroencephalography

IntroductionParalyzed and physically impaired patients face communication difficulties, even when they are mentally coherent and aware. Electroencephalographic (EEG) brain–computer interfaces (BCIs) offer a potential communication method for these people without invasive surgery or physical device controls.MethodsAlthough virtual keyboard protocols are well documented in EEG BCI paradigms, these implementations are visually taxing and fatiguing. All English words combine 44 unique phonemes, each corresponding to a unique EEG pattern. In this study, a complete phoneme-based imagined speech EEG BCI was developed and tested on 16 subjects.ResultsUsing open-source hardware and software, machine learning models, such as k-nearest neighbor (KNN), reliably achieved a mean accuracy of 97 ± 0.001%, a mean F1 of 0.55 ± 0.01, and a mean AUC-ROC of 0.68 ± 0.002 in a modified one-versus-rest configuration, resulting in an information transfer rate of 304.15 bits per minute. In line with prior literature, the distinguishing feature between phonemes was the gamma power on channels F3 and F7.DiscussionHowever, adjustments to feature selection, trial window length, and classifier algorithms may improve performance. In summary, these are iterative changes to a viable method directly deployable in current, commercially available systems and software. The development of an intuitive phoneme-based EEG BCI with open-source hardware and software demonstrates the potential ease with which the technology could be deployed in real-world applications

Electroencephalographic Response of Brain Stimulation by Shock Waves From Laser Generated Carbon Nanotube Transducer

Neuromodulation is used to treat neurological disorders. Focused ultrasound can deliver acoustic energy to local regions of the brain, including deep brain structures. In addition, it is possible to induce the activation or inhibition of nerves through parameter adjustments of focused ultrasound. Laser-generated focused ultrasound (LGFUS) has demonstrated a potential use in precise therapeutic ultrasound applications owing to the ability to produce high-pressure, broadband frequency of shock waves with a tight focal spot, resulting in confined acoustic exposure of a small area. However, there have been few studies of neurostimulation using shock waves with pulse durations of several nanoseconds. The purpose of this study is to investigate the possibility of neurostimulation by shock waves generated from a focused Carbon Nanotube (fCNT) transducer. We measured electroencephalographic (EEG) signals in three rat brains before and after shock wave stimulation and compared them in the time and frequency domains. In the time domain, the number of peaks of EEG signals was measured significantly higher after shock wave stimulation than before stimulation in all three rats. The three rats showed differences in three frequency bands: theta(4-7 Hz), alpha(8-12), and 1&#x2013;30 Hz, before and after shock wave stimulation (p &#x003C; 0.001). These differences in EEG signals after shock wave stimulation of three rats were confirmed mainly because of shock waves. The stimulation of a rat brain was feasible using shock waves generated by the fCNT transducer. This study provides a basis for the applications of shock waves to brain stimulation for precise targeting

Correction to &#x201C;Electroencephalographic Response of Brain Stimulation by Shock Waves From Laser Generated Carbon Nanotube Transducer&#x201D;

In The above article [1], Fig. 5(b) is incorrectly presented as a duplicate of Fig. 5(a). The correct figure is presented here