TB165: Chemical and Physical Properties of the Danforth, Elliotsville, Peacham, and Penquis Soil Map Units

The soils reported in this bulletin have developed in several different parent materials. The Danforth soil has developed from very deep, well drained, loose, high coarse fragment till derived from slate and fine-grained metasandstone. The Elliottsville soils have developed in moderately deep, well drained till derived from slates, metasandstones, phyllite and schists. The Penquis soils developed in moderately deep, well drained till of similar lithology as Elliottsville, but with a higher component of weathered and crushable rock fragments throughout the soil profile. Peacham soils are developed in very deep, very poorly drained, dense till derived from phyllite, schist, and granite.https://digitalcommons.library.umaine.edu/aes_techbulletin/1041/thumbnail.jp

A Robust Ensemble Algorithm for Ischemic Stroke Lesion Segmentation: Generalizability and Clinical Utility Beyond the ISLES Challenge

Diffusion-weighted MRI (DWI) is essential for stroke diagnosis, treatment decisions, and prognosis. However, image and disease variability hinder the development of generalizable AI algorithms with clinical value. We address this gap by presenting a novel ensemble algorithm derived from the 2022 Ischemic Stroke Lesion Segmentation (ISLES) challenge. ISLES'22 provided 400 patient scans with ischemic stroke from various medical centers, facilitating the development of a wide range of cutting-edge segmentation algorithms by the research community. Through collaboration with leading teams, we combined top-performing algorithms into an ensemble model that overcomes the limitations of individual solutions. Our ensemble model achieved superior ischemic lesion detection and segmentation accuracy on our internal test set compared to individual algorithms. This accuracy generalized well across diverse image and disease variables. Furthermore, the model excelled in extracting clinical biomarkers. Notably, in a Turing-like test, neuroradiologists consistently preferred the algorithm's segmentations over manual expert efforts, highlighting increased comprehensiveness and precision. Validation using a real-world external dataset (N=1686) confirmed the model's generalizability. The algorithm's outputs also demonstrated strong correlations with clinical scores (admission NIHSS and 90-day mRS) on par with or exceeding expert-derived results, underlining its clinical relevance. This study offers two key findings. First, we present an ensemble algorithm (https://github.com/Tabrisrei/ISLES22_Ensemble) that detects and segments ischemic stroke lesions on DWI across diverse scenarios on par with expert (neuro)radiologists. Second, we show the potential for biomedical challenge outputs to extend beyond the challenge's initial objectives, demonstrating their real-world clinical applicability

Accurate Visual Stimulus Presentation Software for EEG experiments

In many experimental paradigms of electroencephalography (EEG), timing accuracy in presenting experimental stimuli as visualized on a computer screen is of importance. This paper describes a software package enabling students and researchers to easily set up suitable visual stimulation sequences for simple experiment. We describe the mechanisms employed by the software to achieve good timing accuracy on a common laptop, running Microsoft Windows. With a light sensor, the synchronization of the visual stimulation and the EEG hardware is evaluated. The results demonstrate a maximum variance of 2ms between the synchronization marker send to the EEG hardware and the presentation of the stimulus on screen. We also confirm the software’s ability to display a flickering stimulus, used in steady-state visual evoked potential (SSVEP) experiments, with a precise frequency.status: publishe

Towards the detection of Error-Related Potentials and its integration in the context of a P300 Speller Brain-Computer Interface

A P300 Speller is a Brain-Computer Interface (BCI) that enables subjects to spell text on a computer screen by detecting P300 Event-Related Potentials in their electroencephalograms (EEG). This BCI application is of particular interest to disabled patients who have lost all means of verbal and motor communication. Error-related Potentials (ErrPs) in the EEG are generated by the subject's perception of an error. We report on the possibility of using these ErrPs for improving the performance of a P300 Speller. Overall 9 subjects were tested, allowing us to study their EEG responses to correct and incorrect performances of the BCI, compare our findings to previous studies, explore the possibility of detecting ErrPs and discuss the integration of ErrP classifiers into the P300 Speller system.status: publishe

Feasibility of Error-Related Potential Detection as Novelty Detection Problem in P300 Mind Spelling

In this paper, we report on the feasibility of the Error-Related Potential (ErrP) integration in a particular type of Brain-Computer Interface (BCI) called the P300 Mind Speller. With the latter, the subject can type text only by means of his/her brain activity without having to rely on speech or muscular activity. Hereto, electroencephalography (EEG) signals are recorded from the subject’s scalp. But, as with any BCI paradigm, decoding mistakes occur, and when they do, an EEG potential is evoked, known as the Error-Related Potential (ErrP), locked to the subject’s realization of the mistake. When the BCI would be able to also detect the ErrP, the last typed character could be automatically corrected. However, since the P300 Mind Speller is optimized to correctly operate in the first place, we have much less ErrP’s than responses to correctly typed characters. In fact, exactly because it is supposed to be a rare phenomenon, we advocate that ErrP detection can be treated as a novelty detection problem. We consider in this paper different one-class classification algorithms based on novelty detection together with a correction algorithm for the P300 Mind Speller.status: publishe

Decoding SSVEP responses based on PARAFAC decomposition

In this position paper, we investigate whether a parallel factor analysis (Parafac) decomposition is beneficial to the decoding of steady-state visual evoked potentials (SSVEP) present in electroencephalogram (EEG) recordings taken from the subject's scalp. In particular, we develop an automatic algorithm aimed at detecting the stimulation frequency after Parafac decomposition. The results are validated on recordings made from 54 subjects using consumer-grade EEG hardware (Emotiv's EPOC headset) in a real-world environment. The detection of one frequency among 12, 4 and 2 possible was considered to assess the feasibility for Brain Computer Interfacing (BCI). We determined the frequencies subsets, among all subjects, that maximize the detection rate.status: publishe

Combining Object Detection And Brain Computer Interfacing: Towards A New Way Of Subject-Environment Interaction

In this paper we propose an application which combines two research disciplines: object detection and brain-computer interfacing. It is in particular useful for patients suffering from a severe motor impairment which prevents them to interact with their surrounding environment. The application shows an image of e.g., the room of the patient, on a computer screen and searches for instances of certain objects in the image. When these are found, a flashing dot appears on top of them, flickering in a fixed but different frequency for each object. Meanwhile, brain-activity (EEG) is recorded. Selecting an object can then be achieved by looking at the corresponding flashing dot: the application processes the EEG-readings and identifies the frequency embedded in the signal (SSVEP decoding). Therefore it can conclude on the object the subject was looking at. In this way a patient can (re)gain interaction with his or her environment.status: publishe

Subject-Adaptive Steady-State Visual Evoked Potential Detection for Brain-Computer Interface

We report on the development of a four command Brain-Computer Interface (BCI) based on steady-state visual evoked potential (SSVEP) responses detected from human electroencephalograms (EEGs). The proposed system combines spatial filtering, feature extraction and selection, and a classifier. Two types of classifiers were compared: one based on equal treatment of all harmonics in all EEG channels and the second based on preliminary training resulting in a weighted treatment of the harmonics. Results from six healthy subjects are evaluated.status: publishe