# Azure Machine Learning tools efficiency in the electroencephalographic signal P300 standard and target responses classification

> scholarly article

**Wikidata**: [Q127310773](https://www.wikidata.org/wiki/Q127310773)  
**Source**: https://4ort.xyz/entity/azure-machine-learning-tools-efficiency-in-the-electroencephalographic-signal-p300-standard-and-target-responses-classif

## Summary
"Azure Machine Learning tools efficiency in the electroencephalographic signal P300 standard and target responses classification" is a scholarly article published on August 30, 2019. The paper investigates the application of machine learning techniques within the specific domain of classifying P300 electroencephalographic (EEG) signals. It was authored by a team including Grzegorz M. Wójcik and Jolanta Masiak and appears in Volume 15, Issue 3 of the journal *Bio-Algorithms and Med-Systems*.

## Key Facts
- **Title:** Azure Machine Learning tools efficiency in the electroencephalographic signal P300 standard and target responses classification
- **Publication Date:** August 30, 2019
- **Instance Of:** Scholarly article
- **Language:** English
- **Published In:** *Bio-Algorithms and Med-Systems*
- **Volume:** 15
- **Issue:** 3
- **Main Subject:** Machine learning
- **Described At:** https://www.degruyter.com/document/doi/10.1515/bams-2019-0031/html
- **Authors (Ordered):**
  1. Grzegorz M. Wójcik
  2. Andrzej Kawiak
  3. Lukasz Kwasniewicz
  4. Piotr Schneider
  5. Jolanta Masiak

## FAQs
**Who are the authors of this scholarly article?**
The article was authored by Grzegorz M. Wójcik, Andrzej Kawiak, Lukasz Kwasniewicz, Piotr Schneider, and Jolanta Masiak.

**Where was the article published?**
It was published in the academic journal *Bio-Algorithms and Med-Systems* in Volume 15, Issue 3.

**What is the specific subject of the research?**
The main subject of the work is machine learning, specifically focusing on the efficiency of tools used to classify P300 standard and target responses in electroencephalographic signals.

## Why It Matters
This article serves as a specialized study at the intersection of neuroscience and computational technology. By evaluating the efficiency of Azure Machine Learning tools, the research addresses the technical challenges involved in processing bio-signals, specifically the P300 wave, which is a critical component in brain-computer interface (BCI) research and neurodiagnostics. The publication contributes to the broader field of medical algorithms by exploring how modern cloud-based machine learning platforms can be utilized to decode complex neurological data.

## Notable For
- Investigating the specific efficiency of Microsoft Azure Machine Learning tools in a medical context.
- Focusing on the P300 response, a well-known component of event-related potentials (ERPs) in EEG signals.
- Combining expertise from multiple authors, likely spanning computer science and medical fields.
- Publication in a dedicated journal for biological algorithms and medical systems.

## Body

### Publication Details
The scholarly article "Azure Machine Learning tools efficiency in the electroencephalographic signal P300 standard and target responses classification" was officially published on August 30, 2019. It appears in *Bio-Algorithms and Med-Systems*, a journal dedicated to the intersection of biological research and computational methods. The article is located in **Volume 15**, **Issue 3** of the publication. The work is classified strictly as a scholarly article and is written in the English language.

### Research Focus
The primary subject matter of the article is **machine learning**. The title explicitly outlines the scope of the research, which involves assessing the efficiency of specific software tools—specifically those from Azure Machine Learning—in the classification of electroencephalographic (EEG) signals. The study targets the **P300** signal, distinguishing between "standard" and "target" responses. This suggests a comparative or performance-based analysis of classification algorithms applied to neurological data.

### Authorship and Credits
The research was a collaborative effort involving five distinct authors, listed in the following order of contribution:
1.  **Grzegorz M. Wójcik**
2.  **Andrzej Kawiak**
3.  **Lukasz Kwasniewicz**
4.  **Piotr Schneider**
5.  **Jolanta Masiak**

### Access and Identifiers
The article is accessible online via the De Gruyter publishing platform. The specific URL for the HTML version of the document is `https://www.degruyter.com/document/doi/10.1515/bams-2019-0031/html`. This link serves as the primary digital reference for the work as cataloged in academic databases.

## References

1. April 2024 Public Data File from Crossref