Brain tumors are among the most severe and life-threatening conditions affecting both children and adults. They constitute approximately 85-90% of all primary Central Nervous System (CNS) tumors, with an estimated 11,700 new cases diagnosed annually. The 5-year survival rate for individuals with malignant brain or CNS tumors is alarmingly low, at 34% for men and 36% for women. Brain tumors are categorized into various types, including benign, malignant, and pituitary tumors.
Stress became a common factor of individuals in this competitive work environment, especially in academics. To address and assess this issue, this MUSEI-EEG dataset provides the Electroencephalogram (EEG) data of 20 undergraduate individuals in the 18-24 years age group (both male and female). Raag Darbari's music-based three-stage paradigm is designed for the subjects for cognitive stress assessment. Through this paradigm, physiological signal-based monitoring of stress level reduction can be observed in reference to stress and anxiety forms filled by the individual.
data for different PUF designs that has been implemented on different FPGA for making a final comparision Table for new PUF disigns and some conventional ones. These data can be useful for any Hardware security implementation to make the decision regarding a PUF. These can be used when anyone need to extract Crptographic KEY.
The dataset consists of EEG recorded non-invasively from five participants while they are performing a handwriting imagery task, in which each participant was instructed to imagine the process of handwriting the 26 English letters. Major preprocessing steps have been conducted, including large-amplitude artifact removal using Independent Component Analysis and bandpass filtering between 0.1-45 Hz.
This dataset consists of MRI images of brain tumors, specifically curated for tasks such as brain tumor classification and detection. The dataset includes a variety of tumor types, including gliomas, meningiomas, and glioblastomas, enabling multi-class classification. Each MRI scan is labeled with the corresponding tumor type, providing a comprehensive resource for developing and evaluating machine learning models for medical image analysis. The data can be used to train deep learning algorithms for brain tumor detection, aiding in early diagnosis and treatment planning.
We developed a unique and valuable dataset specifically for advancing Brain-Computer Interface (BCI) systems by recording brain activity from a dedicated volunteer. The participant was asked to pronounce 100 carefully selected Malayalam words, along with their English translations, which were chosen for their relevance to astronauts during human space missions. The volunteer pronounced these words both vocally and subvocally, each word being repeated 50 times. Non-invasive Electroencephalography (EEG) sensors were employed to capture the brain activity associated with these tasks.
This is a collection of scripts to perform essential preprocessing steps,
doing denoise and feature extraction of EEG (ERP) data using MATLAB and the EEGLAB toolbox.
The scripts are under current development with no guarantee of proper
functioning. The scripts are published in the hopes of helping people with interests in our experiment to reproduce the steps under their environment, and maybe extend to further improvement in the future. Currently, this project works with Muse S band (cortex TP9, TP10, AF7, AF8) device and is implemented with XDF raw Data.
The removal of surgical tools from the brain is a critical aspect of post-operative care. Surgical sponges such as cotton balls are one of the most commonly retained tools, as they become visually indistinguishable from the surrounding brain tissue when soaked with blood and can fragment into smaller pieces. This can lead to life-threatening immunological responses and invasive reoperation, demonstrating the need for new foreign body object detection methods.
Brain-Computer Interface (BCI) is a technology that enables direct communication between the brain and external devices, typically by interpreting neural signals. BCI-based solutions for neurodegenerative disorders need datasets with patients’ native languages. However, research in BCI lacks insufficient language-specific datasets, as seen in Odia, spoken by 35-40 million individuals in India. To address this gap, we developed an Electroencephalograph (EEG) based BCI dataset featuring EEG signal samples of commonly spoken Odia words.
This dataset contains EEG error-related potential signals elicited by humans while observing an AI agent play an atari-based maze game.
