The global Brain Tumor Diagnosis and Treatment Market valued USD 1,395.7 mn in 2023 and expected to USD 2,036.5 mn by 2032 at a CAGR of 7.14%.

The original dataset from the reference consists of 5 different folders, each with 100 files, with each file representing a single subject/person. Each file is a recording of brain activity for 23.6 seconds. The corresponding time-series is sampled into 4097 data points. Each data point is the value of the EEG recording at a different point in time. So we have total 500 individuals with each has 4097 data points for 23.5 seconds. We divided and shuffled every 4097 data points into 23 chunks, each chunk contains 178 data points for 1 second, and each data point is the value of the EEG recording at a different point in time. So now we have 23 x 500 = 11500 pieces of information(row), each information contains 178 data points for 1 second(column), the last column represents the label y {1,2,3,4,5}. The response variable is y in column 179, the Explanatory variables X1, X2, ..., X178 y contains the category of the 178-dimensional input vector. Specifically y in {1, 2, 3, 4, 5}: 5 - eyes open, means when they were recording the EEG signal of the brain the patient had their eyes open 4 - eyes closed, means when they were recording the EEG signal the patient had their eyes closed 3 - Yes they identify where the region of the tumor was in the brain and recording the EEG activity from the healthy brain area 2 - They recorder the EEG from the area where the tumor was located 1 - Recording of seizure activity All subjects falling in classes 2, 3, 4, and 5 are subjects who did not have epileptic seizure. Only subjects in class 1 have epileptic seizure. Our motivation for creating this version of the data was to simplify access to the data via the creation of a .csv version of it. Although there are 5 classes most authors have done binary classification, namely class 1 (Epileptic seizure) against the rest.

Data assimilation, defined as the fusion of data with preexisting knowledge, is particularly suited to elucidating underlying phenomena from noisy/insufficient observations. Although this approach has been widely used in diverse fields, only recently have efforts been directed to problems in neuroscience, using mainly intracranial data and thus limiting its applicability to invasive measurements involving electrode implants. Here we intend to apply data assimilation to non-invasive electroencephalography (EEG) measurements to infer brain states and their characteristics. For this purpose, we use Kalman filtering to combine synthetic EEG data with a coupled neural-mass model together with Ary's model of the head, which projects intracranial signals onto the scalp. Our results show that using several extracranial electrodes allows to successfully estimate the state and a specific parameter of the model, whereas one single electrode provides only a very partial and insufficient view of the system. The superiority of using multiple extracranial electrodes over using only one, be it intra- or extra-cranial, is shown in different dynamical behaviours. Our results show potential toward future clinical applications of the method.

• Paper: Self-Supervised Contrastive Pre-Training For Time Series via Time-Frequency Consistency
• Paper link:
• Github repo: https://github.com/mims-harvard/TFC-pretraining
• Project website:

Epilepsy contains single-channel EEG measurements from 500 subjects. For each subject, the brain activity was recorded for 23.6 seconds. The dataset was then divided and shuffled (to mitigate sample-subject association) into 11,500 samples of 1 second each, sampled at 178 Hz. The raw dataset features 5 different classification labels corresponding to different status of the subject or location of measurement - eyes open, eyes closed, EEG measured in healthy brain region, EEG measured where the tumor was located, and, finally, the subject experiencing seizure episode. To emphasize the distinction between positive and negative samples in terms of epilepsy, We merge the first 4 classes into one and each time series sample has a binary label describing if the associated subject is experiencing seizure or not.