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When available, the names were derived from the literature. Otherwise, we gave arbitrary, descriptive names to unnamed BCIs to facilitate communication.

A desirable BCI is strongly correlated with ‘true’ body condition (i.e., percent fat, scaled fat, or residual fat) but is not correlated with size (i.e., SVL). Values with the greatest correlation coefficient for each column are italicized. Generally speaking, BCIs that are strongly correlated with percent fat are also strongly correlated with SVL in our dataset. Abbreviations for each of the BCIs are provided in Table 1.

We used several tests to test the hypotheses that: 1) the data are linear; 2) the variance is constant (i.e., homoscedastic); and 3) the frequency distribution of residuals is normal. For clarity, only p-values are reported. Most assumptions are met for regressions of log-transformed mass on log-transformed length, but regressions of mass on length cubed exhibited neither homoscedasticity nor normality. Abbreviations for each of the BCIs are provided in Table 1.

Body condition is a measure of the health and fitness of an organism represented by available energy stores, typically fat. Direct measurements of fat are difficult to obtain non-invasively, thus body condition is usually estimated by calculating body condition indices (BCIs) using mass and length. The utility of BCIs is contingent on the relationship of BCIs and fat, thereby validation studies should be performed to select the best-performing BCI before application in ecological investigations. We evaluated 11 BCIs in 883 Argentine black and white tegus (Salvator merianae) removed from their non-native range in South Florida, United States. Because the length-mass relationship in tegus is allometric, a segmented linear regression model was fit to the relationship between mass and length to define size classes. We evaluated percent, residual, and scaled fat and determined percent fat was the best measure of fat because it was the least associated with snout-vent length (SVL). We evaluat...

Body condition is a measure of the health and fitness of an organism represented by available energy stores, typically fat. Direct measurements of fat are difficult to obtain non-invasively, thus body condition is usually estimated by calculating body condition indices (BCIs) using mass and length. The utility of BCIs is contingent on the relationship of BCIs and fat, thereby validation studies should be performed to select the best-performing BCI before application in ecological investigations. We evaluated 11 BCIs in 883 Argentine black and white tegus (Salvator merianae) removed from their non-native range in South Florida, United States. Because the length-mass relationship in tegus is allometric, a segmented linear regression model was fit to the relationship between mass and length to define size classes. We evaluated percent, residual, and scaled fat and determined percent fat was the best measure of fat because it was the least associated with snout-vent length (SVL). We evaluated performance of BCIs with the full dataset and within size classes and identified Fulton’s K as the best-performing BCI for our sampled population, explaining up to 19% of the variation in fat content. Overall, we found that BCIs: 1) maintained relatively weak relationships with measures of fat and 2) splitting data into size classes reduced the strength of the relationship (i.e., bias) between percent fat and SVL but did not improve the performance of BCIs. We postulate that the weak performance of BCIs in our dataset was likely due to the weak association of fat with SVL, the body plan and life-history traits of tegus, and potentially inadequate accounting of available energy resources. We caution against assuming that BCIs are strong indicators of body condition across species and suggest that validation studies be implemented, or that alternative or complementary measures of health or fitness should be considered. Methods Species description The Argentine black and white tegu is a large teiid lizard native to eastern and central South America that has become established in several areas of central and southern Florida, United States. Tegus can be classified as omnivores as well as generalist meso-predators which consume fruit, plant material, and animal prey (arthropods, gastropods, reptiles, birds, and small mammals), with evidence of diet changing based on seasonal abundance. Tegus are also known to scavenge upon carrion and regularly consume eggs of ground-nesting vertebrates such as alligators, turtles, and birds. This species is considered invasive in Florida with potential negative impacts on native fauna through egg predation and competition for burrowing sites. To combat this threat, management programs have been established in Florida to reduce the population of invasive tegus and halt their expansion into ecologically sensitive areas, such as nesting sites of threatened species such as American crocodile (Crocodylus acutus) and gopher tortoises (Gopherus polyphemus). Collection, euthanasia, and necropsy We received tegus collected through trapping and removal efforts performed by the Florida Fish and Wildlife Conservation Commission (FWC) and the University of Florida (UF) from 2012 through 2018. Once obtained, tegus were humanely euthanized using captive bolt or firearm immediately followed by pithing, and frozen until necropsy. Tegus were thawed prior to necropsy and examined for general health and condition by visually inspecting all internal organs and the body exterior for any abnormalities or deformities that may affect body mass, body length, or fat mass. We obtained measurements of snout-vent length (SVL) to the nearest 0.1 cm using a flexible measuring tape, and total body mass using a digital scale to the nearest g. Coelomic wet-fat mass was obtained by removing and weighing the discrete abdominal fat bodies to the nearest 0.0001 g. The average timespan a tegu was held between euthanasia and necropsy was approximately 263 ± 258 days (range 0–1,847 days). The research protocol was approved by the University of Florida Animal Research Committee and University of Florida Institutional Animal Care and Use Committee and protocol numbers are listed in the Acknowledgements. Statistical methods To reduce potential bias in our dataset, we excluded data from tegus with incomplete or unreliable necropsies due to decay, unknown sex, physical abnormalities (missing or abnormal limbs or tails (e.g., regenerated tail), and scoliosis), or if the time spent in captivity prior to euthanasia was more than 4 days. We also removed animals whose wet-fat mass was equal to 0, which could not be transformed with natural log. All data analyses were performed in R. Additional information on the following analyses using this data is available within the associated manuscript.

Naming and abbreviations follow Falk et al. [3]. Additional abbreviations indicating regression types are defined as follows: standard major axis (SMA), major axis (MA), and ordinary least squares (OLS).

Steady-state visual evoked potentials (SSVEP) based paradigm is a conventional BCI method with the advantages of high information transfer rate, high tolerance to artifacts and the robust performance across users. But the occurrence of mental load and fatigue when users stare at flickering stimuli is a critical problem in implementation of SSVEP-based BCIs. Based on electroencephalography (EEG) power indices α, θ, θ + α, ratio index θ/α and response properties of amplitude and SNR, this study quantitatively evaluated the mental load and fatigue in both of conventional flickering and the novel motion-reversal visual attention tasks. Results over nine subjects revealed significant mental load alleviation in motion-reversal task rather than flickering task. The interaction between factors of “stimulation type” and “fatigue level” also illustrated the motion-reversal stimulation as a superior anti-fatigue solution for long-term BCI operation. Taken together, our work provided an objective method favorable for the design of more practically applicable steady-state evoked potential based BCIs.

The data in this file were extracted from 216 publications reported the relationships between body condition indices (BCIs) of fish and parasite infections (macro-parasite).

There remains an active investigation on elevating the classification accuracy and information transfer rate of brain-computer interfaces based on steady-state visual evoked potential. However, it has often been ignored that the performance of steady-state visual evoked potential (SSVEP)-based brain-computer interfaces (BCIs) can be affected through the minor displacement of the electrodes from their optimal locations in practical applications because of the mislocation of electrodes and/or concurrent use of electroencephalography (EEG) devices with external devices, such as virtual reality headsets. In this study, we evaluated the performance robustness of SSVEP-based BCIs with respect to the changes in electrode locations for various channel configurations and classification algorithms. Our experiments involved 21 participants, where EEG signals were recorded from the scalp electrodes densely attached to the occipital area of the participants. The classification accuracies for all the possible cases of electrode location shifts for various channel configurations (1–3 channels) were calculated using five training-free SSVEP classification algorithms, i.e., the canonical correlation analysis (CCA), extended CCA, filter bank CCA, multivariate synchronization index (MSI), and extended MSI (EMSI). Then, the performances of the BCIs were evaluated using two measures, i.e., the average classification accuracy (ACA) across the electrode shifts and robustness to the electrode shift (RES). Our results showed that the ACA increased with an increase in the number of channels regardless of the algorithm. However, the RES was enhanced with an increase in the number of channels only when MSI and EMSI were employed. While both ACA and RES values for the five algorithms were similar under the single-channel condition, both ACA and RES values for MSI and EMSI were higher than those of the other algorithms under the multichannel (i.e., two or three electrodes) conditions. In addition, EMSI outperformed MSI when comparing the ACA and RES values under the multichannel conditions. In conclusion, our results suggested that the use of multichannel configuration and employment of EMSI could make the performance of SSVEP-based BCIs more robust to the electrode shift from the optimal locations.

Brain-Computer Interfaces (BCIs) strive to decode brain signals into control commands for severely handicapped people with no means of muscular control. These potential users of noninvasive BCIs display a large range of physical and mental conditions. Prior studies have shown the general applicability of BCI with patients, with the conflict of either using many training sessions or studying only moderately restricted patients. We present a BCI system designed to establish external control for severely motor-impaired patients within a very short time. Within only six experimental sessions, three out of four patients were able to gain significant control over the BCI, which was based on motor imagery or attempted execution. For the most affected patient, we found evidence that the BCI could outperform the best assistive technology (AT) of the patient in terms of control accuracy, reaction time and information transfer rate. We credit this success to the applied user-centered design approach and to a highly flexible technical setup. State-of-the art machine learning methods allowed the exploitation and combination of multiple relevant features contained in the EEG, which rapidly enabled the patients to gain substantial BCI control. Thus, we could show the feasibility of a flexible and tailorable BCI application in severely disabled users. This can be considered a significant success for two reasons: Firstly, the results were obtained within a short period of time, matching the tight clinical requirements. Secondly, the participating patients showed, compared to most other studies, very severe communication deficits. They were dependent on everyday use of AT and two patients were in a locked-in state. For the most affected patient a reliable communication was rarely possible with existing AT.

We tested three assumptions, whether 1) data are linear, 2) variance is constant (i.e., homoscedastic), and 3) frequency distribution of residuals are normal. Only p-values are reported.

This dataset contains two significant mobile gestures for brain-mobile phone interfaces (BMPIs: (i) motor imagery of tapping on the screen of a mobile device and (ii) motor imagery of swiping down with a thumb on the screen of a mobile device. The raw EEG signals were recorded using the Emotiv EPOC Flex (Model 1.0) headset with saline-based sensors and Emotiv Pro (2.5.1.227) software. The sampling rate is 128 Hz. Each epoch contains 3.5 s signals. The first 1 s signal is recorded before the MI task starts (5 s to 6 s interval in the timing plan), and the next 2.5 s signal is recorded during the MI execution (6 s to 8.5 s interval in the timing plan). Please refer to the reference study below for details.The file names are constructed as follows. For example, taking "D01_s1" and "D01" in the file name refers to subject "01", and "s1" refers to session 1 ("s2" refers to session 2). The label data is given in a separate folder in Matlab format.The data is provided in two different forms for use (the desired is preferable):The set_files folder contains the data prepared for import in EEGLAB. EEGLAB must be installed, and the set files must be imported to access the data. The data is in epoched format in 3D (channels, sample_points, trials). With the EEGLAB interface, all the data can be accessed, and EEGLAB functions can be executed. Also, the EEG variable, which is built after importing the *.set file, contains all the information about the experiment. With the EEG.data variable, epoched data in the dimensions (channels, sample_points, trials) can be accessed.The mat_files folder contains data in mat file format. In these files, epoched data is stored in a 3-D array of size (channels, sample_points, trials). You can access the data as follows. For example, all data from the first session of subject D01 can be retrieved as follows. Load the mat file with the load('D01_s1.mat') code, and access the data using the EEG variable in the workspace. For instance, 13x448 x101 sized epoched data (channels, sample_points, trials) can be retrieved with the command EEG.data. Other information about the experiments and subjects is also included in the fields of the EEG variable.This research was supported by the Turkish Scientific and Research Council (TUBITAK) under project number 119E397.The following article must be used in academic studies with reference. Permission must be obtained for use in commercial studies.Journal: Neural Computing and Applications.DOI : 10.1007/s00521-024-10917-5.Title : MI-BMPI motor imagery brain–mobile phone dataset and performance evaluation of voting ensembles utilizing QPDM.

The brain-computer interfaces (BCIs) provide humans a new communication channel by encoding and decoding brain activities. Steady-state visual evoked potential (SSVEP)-based BCI stands out among many BCI paradigms because of its non-invasiveness, little user training, and high information transfer rate (ITR). However, the use of conductive gel and bulky hardware in the traditional Electroencephalogram (EEG) method hinder the application of SSVEP-based BCIs. Besides, continuous visual stimulation in long time use will lead to visual fatigue and pose a new challenges to the practical application. This study presents an open dataset collected with a wearable SSVEP-based BCI system that compared wet and dry electrodes comprehensively with continuous recording of multiple sessions. The dataset consists of 8-channel SSVEP data from 102 healthy subjects while they performed a cue-guided target selecting task with a 12-target SSVEP-based BCI. For each subject, wet and dry electrodes were used to record 10 consecutive blocks respectively in an overall duration of around two hours. The dataset can be used to evaluate the performance of wet and dry electrodes in SSVEP-based BCIs. The dataset also provide sufficient data for developing new target identification algorithms to improve the performance of wearable SSVEP-based BCIs.

Ordinary least regression of the moral acceptability and use willingness regarding BSDs (NExperiment 1 = 1,090) and regarding BCIs (NExperiment 2 = 1,089).

Brain-computer interfaces (BCIs) realize the information transmission between the brain and the external world. Visual evoked potential (VEP) based BCIs have gained widespread attention in the field of multi-instruction interaction attributed to their high information transfer rate (ITR). However, improving the ITR and the practicability is challenging for existing VEP based BCIs due to factors such as the encoding efficiency and the calibration time. Therefore, to address this issue, this study proposed a new encoding method employing narrow-band random sequences and provided a large dataset for VEP based brain-computer interfaces. Narrow-band random sequences are random sequences with a specific frequency band. The dataset encompasses three paradigms that employ three kinds of encoding sequences: narrow-band sequences with a frequency band of 15~25 Hz (NBRS-15), narrow-band random sequences with a frequency band of 8~16 Hz (NBRS-8), and sequences utilizing joint frequency-phase modulation method with a frequency range of 8-15.8 Hz (JFPM-8). The dataset includes 59-channel electroencephalogram (EEG) data for 100 subjects, and the quality of the dataset is validated through quantitative analyses on EEG characteristics and classification performance.