Description:
This dataset consists of EEG recordings of five essential words commonly used in speech therapy for post-stroke patients. The data was collected from a single female participant using a NeuroSky Mindwave Mobile 2 headset in a controlled environment, ensuring minimal noise interference and high-quality signal acquisition.

Purpose:
The dataset aims to facilitate research in neuroscience and brain-computer interface (BCI) development, specifically for post-stroke rehabilitation. It provides a foundation for creating machine learning models that decode EEG signals into meaningful linguistic outputs, offering potential breakthroughs in assistive technologies for individuals with speech and motor impairments.

Data Structure:
EEG Signal Data: Captures brainwave patterns corresponding to five essential words.
Participant: Post-stroke patients engaged in tasks designed to evoke EEG responses tied to the target words.
Recording Settings: Standardized environment with participants focusing on auditory and visual stimuli associated with each word.

Applications:
Development of classification algorithms for EEG signals linked to word recognition.
Training BCIs to assist in real-time communication for post-stroke patients.
Studying neural activity associated with speech therapy exercises.

Potential Use Cases:
Creation of assistive communication devices for individuals with speech and motor challenges.
Research into personalized EEG signal responses during language-based rehabilitation.
Advancement of neurotechnology for targeted healthcare applications.

Dataset Features:
High-quality EEG recordings of five essential rehabilitation words.
Precise, noise-minimized data collection.
Open-access format to encourage reproducibility and collaboration in EEG and healthcare research.