Digital signal processing

We developed IIST BCI Dataset-9, a novel EEG-based Brain-Computer Interface (BCI)
dataset to improve wheelchair control systems using Malayalam dialect variations. BCI
systems help people with motor disabilities by allowing them to control devices using brain
signals. The limited number of BCI datasets in Indian languages makes it harder for native
speakers to use these systems. To address this, we created a dataset with 15 Malayalam
words related to basic wheelchair commands like Forward, Backward, Go, Stop, Reverse,

Synthetic EEG Dataset for CNN-LSTM Training: Clean and Artifact-Contaminated Signals

This dataset consists of synthetically generated EEG and EMG signals designed for training Convolutional Neural Networks (CNNs) in artifact detection and removal. The dataset includes both clean EEG signals and EEG signals contaminated with simulated EMG artifacts from various sources.

This dataset comprises high-resolution 3-axis accelerometer recordings collected from human participants performing distinct hand gestures, intended for training gesture-based assistive interfaces. Each participant’s raw motion signals are individually organized, enabling both user-specific and generalizable model development. The dataset includes time-series accelerometer data, along with a feature-augmented version containing extracted statistical and temporal descriptors such as RMS, Jerk, Entropy, and SMA. 

Motion analysis provide important information in rehabilitation, performance evaluation, and movement symmetry assessment, with applications including neurology, biomedicine, surgery, and sports monitoring. The integration of wearable sensors and signal processing forms a robust interdisciplinary platform for such analysis. Specific methods are based on monitoring physiological and motion responses during controlled exercises that simulate real-world motion scenarios.

Electroplating chemicals are the silent architects behind the shiny, corrosion-resistant surfaces we often take for granted. From the gleaming chrome on car parts to the delicate gold plating on jewelry, these chemicals are the backbone of a process that blends chemistry with craftsmanship. At its core, electroplating is the method of depositing a thin layer of metal onto the surface of another material using electric current, and the chemicals involved make all the difference. But this isn’t just science—it’s also art.

Electroplating chemicals are the silent architects behind the shiny, corrosion-resistant surfaces we often take for granted. From the gleaming chrome on car parts to the delicate gold plating on jewelry, these chemicals are the backbone of a process that blends chemistry with craftsmanship. At its core, electroplating is the method of depositing a thin layer of metal onto the surface of another material using electric current, and the chemicals involved make all the difference. But this isn’t just science—it’s also art.

To support research on multimodal speech emotion recognition (SER), we developed a dual-channel emotional speech database featuring synchronized recordings of bone-conducted (BC) and air-conducted (AC) speech. The recordings were conducted in a professionally treated anechoic chamber with 100 gender-balanced volunteers. AC speech was captured via a digital microphone on the left channel, while BC speech was recorded from an in-ear BC microphone on the right channel, both at a 44.1 kHz sampling rate to ensure high-fidelity audio. 

Astronomical instrumentation and related fields have seen remarkable evolution in recent decades, driving the need for advanced signal acquisition and processing techniques. Current experiments demand readout capabilities beyond traditional approaches, leading to the adoption of a wideband instrumentation system architecture for high-speed Radio Frequency (RF) measurements.

This dataset was collected to support research on the screening and diagnosis of Diabetic Peripheral Neuropathy (DPN) and Cardiac Autonomic Neuropathy (CAN) using wearable sensor technology. It includes synchronized data from gait analysis and physiological signals such as electrocardiogram (ECG), heart rate variability (HRV), and inertial measurement units (IMUs) obtained from individuals with and without DPN and CAN.

Data from acoustic condition monitoring of a milling process. During the process, several test parts were milled and acoustic data was recorded using a MEMS microphone.
One file shows two different tools used during milling, one for rough (face) milling with five cutting edges, and one with four cutting edges.

The other files show the data from different parts being milled, with the tool having four cutting edges. There were seven parts milled in total.
As the parts progress, it can be seen that the acoustic data changes, indicating wear and damage in the tool.