---
title: "ArEEG_Words: Dataset for Envisioned Speech — Darwish 2024"
description: "Public EEG resources for imagined (\"envisioned\") speech exist mostly for English and a few other languages, and Arabic has none."
authors: ["Hazem Darwish", "Abdalrahman Al Malah", "Khloud Al Jallad", "Nada Ghneim"]
year: 2024
venue: "arXiv:2411.18888"
source: "https://arxiv.org/abs/2411.18888"
doi: "10.48550/arxiv.2411.18888"
arxiv: "2411.18888"
licence: "CC BY 4.0 (arXiv)"
licence_url: "http://creativecommons.org/licenses/by/4.0/"
summary_licence: "Elamur Cosmic License"
canonical: "https://research.elamur.ai/free/en/papers/darwish-2024-areeg-words-dataset-envisioned-speech/"
---
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# ArEEG\_Words: Dataset for Envisioned Speech Recognition using EEG for Arabic Words

**Authors:** [Hazem Darwish](https://md.research.elamur.ai/free/en/authors/hazem-darwish/), [Abdalrahman Al Malah](https://md.research.elamur.ai/free/en/authors/abdalrahman-al-malah/), [Khloud Al Jallad](https://md.research.elamur.ai/free/en/authors/khloud-al-jallad/), [Nada Ghneim](https://md.research.elamur.ai/free/en/authors/nada-ghneim/) · **Year:** 2024 · **Venue:** arXiv:2411.18888 · **Licence:** [CC BY 4.0 (arXiv)](http://creativecommons.org/licenses/by/4.0/)

**Links:** [Publisher's page](https://arxiv.org/abs/2411.18888) · [PDF](https://arxiv.org/pdf/2411.18888) · [DOI 10.48550/arxiv.2411.18888](https://doi.org/10.48550/arxiv.2411.18888) · [arXiv 2411.18888](https://arxiv.org/abs/2411.18888) · [OpenAlex](https://openalex.org/W4405029753)

**Citations:** 0 citations (openalex-snapshot · 2026-10-14)

## Summary
Public EEG resources for imagined ("envisioned") speech exist mostly for English and a few other languages, and Arabic has none. The authors address this by releasing ArEEG_Words, which they describe as the first Arabic EEG dataset of imagined words. Twenty-two native Arabic speakers, mostly young university-educated adults, wore a 14-channel Emotiv EPOC X headset. Each participant saw 16 common Arabic words, such as directions, on slides in random order. After each slide, the participant closed their eyes and imagined the word for 10 seconds, then rested for 20 seconds. Participants were asked to avoid caffeine, alcohol and smoking for 8 hours beforehand, and they were checked periodically to confirm they were still concentrating. The signals were captured at 2048 Hz, downsampled to 128 Hz, and cut into 250 ms windows. The release is the data itself; the paper reports no classification experiments or baselines. Its value is as a starting resource for Arabic brain-computer interfaces aimed at people who cannot speak. It is hosted on Mendeley Data.

## Key points
- **Scale:** 22 participants (17 male, 5 female, mean age about 22) × 16 words gives 352 recordings. These were split into 250 ms segments, for a reported total of 15,360 EEG signals.
- **Hardware:** Emotiv EPOC X with 14 electrodes placed by the International 10-20 system (AF3 … AF4) and CMS/DRL references, streamed over Bluetooth at 128 Hz.
- **Trial protocol:** 10 s viewing the word, then 10 s imagining it with eyes closed, then a 20 s break. The whole session lasted about an hour.
- **Related work:** a comparison table covers earlier imagined-speech datasets of digits, letters, directions, vowels, syllables and Dutch words. These range from 3 to 29 subjects and from 8- to 128-channel devices.
- **Gaps and inconsistencies:** baseline deep-learning models are left for future work. The demographic sections contradict each other (university-educated vs. "high school students", ages "16–64" vs. a listed range of 19–25). The segment count also does not divide evenly per recording.

## Interesting ideas
- The order of word slides was randomized so participants could not anticipate the next item, which keeps sequence effects out of the signal. — For Mova (learn): Shuffle the order of evaluation items and negative controls on every run, so scores cannot reflect position or carry-over effects in stateful readers.
- Participants were questioned periodically during the session to confirm they had stayed focused, which acts as a built-in compliance check on the labels. — For Mova (learn): Insert known-answer "attention probes" into batches sent to LLM teacher tiers, and discard or down-weight a batch whose probes fail.
- Long 10 s recordings were sliced into many fixed 250 ms windows, multiplying the sample count from a small number of sessions. This also creates strongly correlated samples that must be split by recording or person. — For Mova (learn): When silver data is cut into sentences or clauses, keep train/test splits at the document level so near-duplicate fragments do not leak and inflate scores.
- A cited dataset chose its stimulus words to vary semantics and syllable count while keeping duration nearly constant. This isolates the factor being studied from a trivial confound. — For Mova (answer): Build probe question sets that change one linguistic property (e.g., negation or quantifier) while holding sentence length and vocabulary fixed, so accuracy differences can be attributed cleanly.
- The related-work section compares prior datasets along fixed columns (subjects, device, channels, sampling rate, unit type, trial duration), giving a quick inventory card. — For Mova (tree): Keep a similar standardized card for every treebank and dialect conversion we ingest (size, genre, annotation version, known deviations), to guide mixing and conversion decisions.

*Our summary of the paper, in our own words — not its text. The work belongs to its authors; read it at the links above.*

Fields: [Linguistics & languages](https://md.research.elamur.ai/free/en/shelves/linguistics/)

## Cite

The paper itself, not this page. BibTeX from the library's record of the work.

```bibtex
@misc{darwish2024areeg,
  title = {{ArEEG\_Words: Dataset for Envisioned Speech Recognition using EEG for Arabic Words}},
  author = {Darwish, Hazem and Malah, Abdalrahman Al and Jallad, Khloud Al and Ghneim, Nada},
  year = {2024},
  doi = {10.48550/arxiv.2411.18888},
  eprint = {2411.18888},
  archivePrefix = {arXiv},
  url = {https://doi.org/10.48550/arxiv.2411.18888}
}
```

- [← previous ArEEG_Chars: Dataset for Envisioned Speech Recognition using EEG for Arabic Characters](https://md.research.elamur.ai/free/en/papers/darwish-2024-areeg-chars-dataset-envisioned-speech/)
- [next → ASOS at NADI 2024 shared task: Bridging Dialectness Estimation and MSA Machine Translation for Arabic Language Enhancement](https://md.research.elamur.ai/free/en/papers/nacar-2024-asos-nadi-2024-shared-task/)
- [↑ back to papers of 2024](https://md.research.elamur.ai/free/en/papers/2024/)

## References

13 references, 0 of them with a card of ours. ↗ leads to the work, «find» searches for one we have no id for yet.

1. Lorainne Tudor Car; Mario Tudor; Katarina Ivana Tudor, "Hans berger The history of electroencephalography," Acta medica Croatica: c̆ asopis Hravatske akademije medicinskih znanosti, p. 8, 2005. [find](https://scholar.google.com/scholar?q=Lorainne+Tudor+Car%3B+Mario+Tudor%3B+Katarina+Ivana+Tudor%2C+%22Hans+berger+The+history+of+electroencephalography%2C%22+Acta+medica+Croatica%3A+c%CC%86+asopis+Hravatske+akademije+medicinskih+znanosti%2C+p%2E+8%2C+2005%2E)
2. Pradeep Kumar, Rajkumar Saini, Partha Pratim Roy, Pawan Kumar Sahu, and Debi Prosad Dogra., "Envisioned speech recognition using EEG sensors.," Personal and Ubiquitous Computing., vol. 15, 2017. [find](https://scholar.google.com/scholar?q=Pradeep+Kumar%2C+Rajkumar+Saini%2C+Partha+Pratim+Roy%2C+Pawan+Kumar+Sahu%2C+and+Debi+Prosad+Dogra%2E%2C+%22Envisioned+speech+recognition+using+EEG+sensors%2E%2C%22+Personal+and+Ubiquitous+Computing%2E%2C+vol%2E+15%2C+2017%2E)
3. Nicolás Nieto; Victoria Peterson. Hugo Leonardo Rufner; Juan Esteban Kamienkowski; Ruben Spies, "Thinking out loud, an open-access EEG-based BCI dataset for inner speech recognition," Scientific data, p. 17, 2022. [find](https://scholar.google.com/scholar?q=Nicol%C3%A1s+Nieto%3B+Victoria+Peterson%2E+Hugo+Leonardo+Rufner%3B+Juan+Esteban+Kamienkowski%3B+Ruben+Spies%2C+%22Thinking+out+loud%2C+an+open%2Daccess+EEG%2Dbased+BCI+dataset+for+inner+speech+recognition%2C%22+Scientific+data%2C)
4. Mokhles M. Abdulghani; Wilbur L. Walters; Khalid H. Abed, "Imagined Speech Classification Using EEG and Deep Learning," bioengineering, p. 15, 2023. [find](https://scholar.google.com/scholar?q=Mokhles+M%2E+Abdulghani%3B+Wilbur+L%2E+Walters%3B+Khalid+H%2E+Abed%2C+%22Imagined+Speech+Classification+Using+EEG+and+Deep+Learning%2C%22+bioengineering%2C+p%2E+15%2C+2023%2E)
5. Siyi Deng; Ramesh Srinivasan., " EEG classification of imagined syllable rhythm using Hilbert spectrum methods.," Journal of Neural Engineering, p. 25, 2010. [find](https://scholar.google.com/scholar?q=Siyi+Deng%3B+Ramesh+Srinivasan%2E%2C+%22+EEG+classification+of+imagined+syllable+rhythm+using+Hilbert+spectrum+methods%2E%2C%22+Journal+of+Neural+Engineering%2C+p%2E+25%2C+2010%2E)
6. Aurélie de Borman; Benjamin Wittevrongel; Ine Dauwe; , Evelien Carrette; Alfred Meurs; Dirk Van Roost; Paul Boon; & Marc M. Van Hulle , "Imagined speech event detection from electrocorticography and its transfer between speech modes and subjects," communications biology, p. 12, 2024. [find](https://scholar.google.com/scholar?q=Aur%C3%A9lie+de+Borman%3B+Benjamin+Wittevrongel%3B+Ine+Dauwe%3B+%2C+Evelien+Carrette%3B+Alfred+Meurs%3B+Dirk+Van+Roost%3B+Paul+Boon%3B+%26+Marc+M%2E+Van+Hulle+%2C+%22Imagined+speech+event+detection+from+electrocorticography+and+i)
7. A. van Wieringen and J. Wouters, "LIST and LINT: sentences and numbers for quantifying speech understanding in severely impaired listeners for Flanders and the Netherlands," Int J. Audio, vol. 47, p. 348–355, 2008 . [find](https://scholar.google.com/scholar?q=A%2E+van+Wieringen+and+J%2E+Wouters%2C+%22LIST+and+LINT%3A+sentences+and+numbers+for+quantifying+speech+understanding+in+severely+impaired+listeners+for+Flanders+and+the+Netherlands%2C%22+Int+J%2E+Audio%2C+vol%2E+47%2C+p%2E+)
8. Charles S. DaSalla, Hiroyuki Kambara, Makoto Sato, Yasuharu Koike., "Single-trial classification of vowel speech imagery using common spatial patterns.," Neural Networks., p. 70, 2009. [find](https://scholar.google.com/scholar?q=Charles+S%2E+DaSalla%2C+Hiroyuki+Kambara%2C+Makoto+Sato%2C+Yasuharu+Koike%2E%2C+%22Single%2Dtrial+classification+of+vowel+speech+imagery+using+common+spatial+patterns%2E%2C%22+Neural+Networks%2E%2C+p%2E+70%2C+2009%2E)
9. Mariko Matsumoto; Junichi Hori, "Classification of silent speech using support vector machine and relevance vector machine," Applied Soft Computing, p. 20, 2014. [find](https://scholar.google.com/scholar?q=Mariko+Matsumoto%3B+Junichi+Hori%2C+%22Classification+of+silent+speech+using+support+vector+machine+and+relevance+vector+machine%2C%22+Applied+Soft+Computing%2C+p%2E+20%2C+2014%2E)
10. Alejandro A Torres-Garc´ıa; Carlos A. Reyes-Garc´ıa; Luis Villasenor-Pineda; Gregorio Garc´ıa-Aguilar, "Implementing a Fuzzy Inference System in a Multi-Objective EEG Channel Selection Model for Imagined Speech Classification," Expert Systems With Applications, p. 41, 2016. [find](https://scholar.google.com/scholar?q=Alejandro+A+Torres%2DGarc%C2%B4%C4%B1a%3B+Carlos+A%2E+Reyes%2DGarc%C2%B4%C4%B1a%3B+Luis+Villasenor%2DPineda%3B+Gregorio+Garc%C2%B4%C4%B1a%2DAguilar%2C+%22Implementing+a+Fuzzy+Inference+System+in+a+Multi%2DObjective+EEG+Channel+Selection+Model+for+Imagi)
11. Luis Alfredo Moctezuma; Alejandro A. Torres-Garc´ıa; Luis Villasenor-Pineda; Maya Carrillo-Ruiz., "Subjects Identification using EEG-recorded Imagined Speech," Expert Systems With Applications, p. 26, 2018. [find](https://scholar.google.com/scholar?q=Luis+Alfredo+Moctezuma%3B+Alejandro+A%2E+Torres%2DGarc%C2%B4%C4%B1a%3B+Luis+Villasenor%2DPineda%3B+Maya+Carrillo%2DRuiz%2E%2C+%22Subjects+Identification+using+EEG%2Drecorded+Imagined+Speech%2C%22+Expert+Systems+With+Applications%2C+p%2E+26%2C)
12. A. A. Torres-García, C. A. Reyes-Garcia and L. Villaseñor-Pineda, "Toward a silent speech interface based on unspoken speech," in Proceedings of BIOSIGNALS 2012 (BIOSTEC), Algarve, Portugal, 2018. [find](https://scholar.google.com/scholar?q=A%2E+A%2E+Torres%2DGarc%C3%ADa%2C+C%2E+A%2E+Reyes%2DGarcia+and+L%2E+Villase%C3%B1or%2DPineda%2C+%22Toward+a+silent+speech+interface+based+on+unspoken+speech%2C%22+in+Proceedings+of+BIOSIGNALS+2012+%28BIOSTEC%29%2C+Algarve%2C+Portugal%2C+2018%2E)
13. A. Gramfort, M. Luessi, E. Larson, D. A. Engemann, D. Strohmeier, C. Brodbeck, L. Parkkonen, M. S. Hämäläinen, , "MNE software for processing MEG and EEG data," NeuroImage, p. 86, 2014. [find](https://scholar.google.com/scholar?q=A%2E+Gramfort%2C+M%2E+Luessi%2C+E%2E+Larson%2C+D%2E+A%2E+Engemann%2C+D%2E+Strohmeier%2C+C%2E+Brodbeck%2C+L%2E+Parkkonen%2C+M%2E+S%2E+H%C3%A4m%C3%A4l%C3%A4inen%2C+%2C+%22MNE+software+for+processing+MEG+and+EEG+data%2C%22+NeuroImage%2C+p%2E+86%2C+2014%2E)