BS-HMS-Dataset is a dataset of the users' brainwave signals and the corresponding hand movement signals from a large number of volunteer participants. The dataset has two parts; (1) Neurosky based Dataset (collected over several months in 2016 from 32 volunteer participants), and (2) Emotiv based Dataset (collected from 27 volunteer participants over several months in 2019). 


There are two folders under each user; session I and sessions II. Each session folder contains four different folders; one for each activity performed by the user. Each activity folder contains .csv files; (1) EEG Data (brainwave.csv), (2) Handmovement Accelerometer Data (accelerometer.csv), and (3) Handmovement Gyroscope Data (gyroscope.csv).

A more deatailed description of the data is given in BS-HMS-Dataset-Documentation.pdf file.

Acknowledgement: This data collection was supported in part by the National Science Foundation (NSF) under grant SaTC-1527795.

Please cite: [1] Diksha Shukla, Sicong Chen, Yao Lu, Partha Pratim Kundu, Ravichandra Malapati, Sujit Poudel, Zhanpeng Jin, Vir Phoha, "Brain Signals and the Corresponding Hand Movement Signals Dataset (BS-HMS-Dataset)", IEEE Dataport, 2019. [Online]. Available: Accessed: Dec. 05, 2019.


This dataset is benchmark dataset we use in our research for Intrusion Detection System.


With the popularity of smartphones and widespread use of high-speed Internet, social media has become a vital part of people’s daily life. Currently, text messages are used in many applications, such as mobile chatting, mobile banking, and mobile commerce. However, when we send a text message via short message service (SMS) or social media, the information contained in the text message transmits as a plain text, which exposes it to attacks.


This dataset is a result of my research production into machine learning in android security. The data was obtained by a process that consisted to map a binary vector of permissions used for each application analyzed {1=used, 0=no used}. Moreover, the samples of malware/benign were devided by "Type"; 1 malware and 0 non-malware.

When I did my research, the datasets of malware and benign Android applications were not available, then I give to the community a part of my research results for the future works.