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Validation of portable, semi-dry electrode-based electroencephalography device for its application in brain-computer interface solutions.

Code ↔ Paper

5 matches between paragraphs of the paper and lines of its authors' code, computed by the harvester (lexical-v1). Click a colored paragraph or line to see its counterpart.

The 5 matches
  1. [1] § Materials and methods › Machine learning methods › Convolutional neural networks ↔ Preprocessing/MI.py, lines 246–294 · score 0.82 · categorical crossentropy, Adam, monitoring, optimizer, patience, epochs
  2. [2] § Materials and methods › Machine learning methods › Support vector machines ↔ Preprocessing/MI.py, lines 187–244 · score 0.74 · Nu SVC, class weight, kernel, vector, SVM
  3. [3] § Materials and methods › Machine learning methods › Convolutional neural networks ↔ Preprocessing/MI.py, lines 246–294 · score 0.70 · Keras, padding, ReLU, activation, dense, dropout
  4. [4] § Materials and methods › BCI paradigms › Visually evoked potentials ↔ Preprocessing/VEP.py, lines 10–63 · score 0.55 · FastICA, SOS, components, threshold, 25 Hz, VEP
  5. [5] § Materials and methods › BCI paradigms › Visually evoked potentials ↔ Preprocessing/MI.py, lines 12–63 · score 0.51 · FastICA, SOS, components, filtered

Paper

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The authors' code

Python · 294 lines · 13 KB · no license · 4 matches

  1. import numpy as np
  2. import matplotlib.pyplot as plt
  3. from scipy import signal
  4. from sklearn.decomposition import FastICA
  5. from scipy.fft import fft
  6. from sklearn import svm
  7. from sklearn.ensemble import RandomForestClassifier
  8. from sklearn.metrics import confusion_matrix, accuracy_score
  9. import keras
  10. from keras import layers
  12. #ifn = "MI_VSN_2024-08-01_13-09-52.csv"
  13. ifn = "MI_Smarting_2024-07-31_14-28-05.csv"
  15. fs = 250
  16. data_str = np.loadtxt(ifn, dtype=str, delimiter=';')
  17. data = np.zeros(data_str.shape)
  18. if ifn.split(sep='_')[1] == "VSN":
  19. data = data_str[:, 0:8].astype(np.float32) * 0.045
  20. data = np.delete(data, obj=6, axis=1)
  21. else:
  22. for i in range(data.shape[0]):
  23. for j in range(data.shape[1]):
  24. data[i, j] = float(data_str[i, j].replace(',', '.'))
  25. data = data[:, 0:-2]
  27. ref = data[:, 5]
  28. for i in range(data.shape[1]):
  29. data[:, i] = data[:, i] - ref
  30. data = np.delete(data, obj=[5, 6], axis=1)
  32. fs = 500
  34. phase = data_str[:, -2].astype(int)
  35. i_trial = data_str[:, -1].astype(int)
  36. edges = np.insert(i_trial[1:i_trial.shape[0]] - i_trial[0:-1], obj=0, values=0)
  37. edges[edges.shape[0] - 1] = 1
  39. hp = signal.butter(2, 1, 'hp', fs=fs, output='sos')
  40. lp = signal.butter(2, 40, 'lp', fs=fs, output='sos')
  41. filtered_data = np.zeros(data.shape)
  42. for i in range(data.shape[1]):
  43. filtered_data[:, i] = data[:, i]
  45. for i in range(data.shape[1]):
  46. filtered_data[:, i] = signal.sosfiltfilt(hp, filtered_data[:, i])
  47. filtered_data[:, i] = signal.sosfiltfilt(lp, filtered_data[:, i])
  49. random_state = 23
  50. ica = FastICA(n_components=None, random_state=random_state)
  51. S_ = ica.fit_transform(filtered_data)
  52. S_plot = np.zeros(S_.shape)
  54. for j in range(S_.shape[1]):
  55. S_plot[:, j] = S_[:, j] + 50 * j
  56. plt.plot(S_plot)
  57. plt.show()
  59. sources_to_delete = [0, 1, 5]
  60. for j in sources_to_delete:
  61. S_[:, int(j)] = 0
  63. filtered_data = ica.inverse_transform(S_)
  65. # bad_trial_margin = 20
  66. # bad_trials2 = np.unique(i_trial[np.where(np.bitwise_or(filtered_data > filtered_data.mean() + bad_trial_margin * filtered_data.std(), filtered_data < filtered_data.mean() - bad_trial_margin * filtered_data.std()))[0]])
  67. threshold = 50
  68. bad_trials2 = np.unique(i_trial[np.where(np.bitwise_or(filtered_data > filtered_data.mean() + threshold, filtered_data < filtered_data.mean() - threshold))[0]])
  70. plt.plot(filtered_data)
  71. plt.plot(phase * np.ptp(filtered_data))
  72. plt.plot(edges * np.ptp(filtered_data))
  73. # plt.plot((filtered_data.mean() + bad_trial_margin * filtered_data.std()) * np.ones(filtered_data.shape[0]))
  74. # plt.plot((filtered_data.mean() - bad_trial_margin * filtered_data.std()) * np.ones(filtered_data.shape[0]))
  75. plt.plot((filtered_data.mean() + threshold) * np.ones(data.shape[0]))
  76. plt.plot((filtered_data.mean() - threshold) * np.ones(data.shape[0]))
  77. plt.show()
  78. plt.show()
  80. i_edges = np.where(edges == 1)[0]
  81. trial_lengths = i_edges[1:i_edges.shape[0]] - i_edges[0:-1]
  82. bad_trial_threshold = 2
  83. bad_trials = np.where(np.bitwise_or(trial_lengths > trial_lengths.mean() + bad_trial_threshold * trial_lengths.std(), trial_lengths < trial_lengths.mean() - bad_trial_threshold * trial_lengths.std()))[0]
  84. trial_length = np.min(np.delete(trial_lengths, bad_trials))
  85. n_trials = i_edges.shape[0] - 1
  86. trial_labels = phase[i_edges[0:i_edges.shape[0]-1] + int(np.round(trial_length/2))]
  87. i_rest = np.where(trial_labels == 0)[0]
  88. i_left = np.where(trial_labels == 1)[0]
  89. i_right = np.where(trial_labels == 2)[0]
  91. print(i_edges)
  92. print(trial_length)
  93. print(bad_trials)
  94. print(bad_trials2)
  95. print(n_trials)
  96. print(trial_labels)
  97. print(i_rest)
  98. print(i_left)
  99. print(i_right)
  101. plt.plot(trial_lengths)
  102. plt.plot((trial_lengths.mean() + bad_trial_threshold * trial_lengths.std()) * np.ones(trial_lengths.shape))
  103. plt.plot((trial_lengths.mean() - bad_trial_threshold * trial_lengths.std()) * np.ones(trial_lengths.shape))
  104. plt.show()
  106. rest_data = np.zeros((i_rest.shape[0], trial_length, data.shape[1]))
  107. for i in range(i_rest.shape[0]):
  108. if not ((i_rest[i] in bad_trials) or (i_rest[i] in bad_trials2)):
  109. rest_data[i, :, :] = filtered_data[i_edges[i_rest[i]]:i_edges[i_rest[i]] + trial_length, :]
  110. rest_data = np.delete(rest_data, obj=np.where(np.sum(np.sum(rest_data, axis=1), axis=1) == 0)[0], axis=0)
  112. left_data = np.zeros((i_left.shape[0], trial_length, data.shape[1]))
  113. for i in range(i_left.shape[0]):
  114. if not ((i_left[i] in bad_trials) or (i_left[i] in bad_trials2)):
  115. left_data[i, :, :] = filtered_data[i_edges[i_left[i]]:i_edges[i_left[i]] + trial_length, :]
  116. left_data = np.delete(left_data, obj=np.where(np.sum(np.sum(left_data, axis=1), axis=1) == 0)[0], axis=0)
  118. right_data = np.zeros((i_right.shape[0], trial_length, data.shape[1]))
  119. for i in range(i_right.shape[0]):
  120. if not ((i_right[i] in bad_trials) or (i_right[i] in bad_trials2)):
  121. right_data[i, :, :] = filtered_data[i_edges[i_right[i]]:i_edges[i_right[i]] + trial_length, :]
  122. right_data = np.delete(right_data, obj=np.where(np.sum(np.sum(right_data, axis=1), axis=1) == 0)[0], axis=0)
  124. # rest_average = rest_data.mean(axis=0)
  125. # left_average = left_data.mean(axis=0)
  126. # right_average = right_data.mean(axis=0)
  127. #
  128. # for i in range(data.shape[1]):
  129. # rest_average[:, i] = rest_average[:, i] - rest_average[0, i]
  130. # left_average[:, i] = left_average[:, i] - left_average[0, i]
  131. # right_average[:, i] = right_average[:, i] - right_average[0, i]
  133. #fig, (ax1, ax2, ax3) = plt.subplots(1, 3)
  134. #ax1.plot(rest_average)
  135. #ax2.plot(left_average)
  136. #ax3.plot(right_average)
  137. #plt.show()
  139. k_lower = int(np.round(8 * trial_length / fs))
  140. #k_upper = int(np.round(int(np.floor(fs/2)) * trial_length / fs))
  141. k_upper = int(np.round(30 * trial_length / fs))
  143. print(k_lower)
  144. print(k_upper)
  146. for i in range(rest_data.shape[0]):
  147. for j in range(rest_data.shape[2]):
  148. rest_data[i, :, j] = np.square(np.abs(fft(rest_data[i, :, j])))
  149. rest_data = rest_data[:, k_lower:k_upper, :]
  150. # for i in range(rest_data.shape[0]):
  151. # rest_data[i, :, :] = rest_data[i, :, :] / np.sum(rest_data[i, :, :])
  153. for i in range(left_data.shape[0]):
  154. for j in range(left_data.shape[2]):
  155. left_data[i, :, j] = np.square(np.abs(fft(left_data[i, :, j])))
  156. left_data = left_data[:, k_lower:k_upper, :]
  157. # for i in range(left_data.shape[0]):
  158. # left_data[i, :, :] = left_data[i, :, :] / np.sum(left_data[i, :, :])
  160. for i in range(right_data.shape[0]):
  161. for j in range(right_data.shape[2]):
  162. right_data[i, :, j] = np.square(np.abs(fft(right_data[i, :, j])))
  163. right_data = right_data[:, k_lower:k_upper, :]
  164. # for i in range(right_data.shape[0]):
  165. # right_data[i, :, :] = right_data[i, :, :] / np.sum(right_data[i, :, :])
  167. rest_data = rest_data[:, :, 2:5]
  168. left_data = left_data[:, :, 2:5]
  169. right_data = right_data[:, :, 2:5]
  171. # for i in range(rest_data.shape[1]):
  172. # for k in range(rest_data.shape[2]):
  173. # avg = (np.average(rest_data, axis=0) + np.average(left_data, axis=0) + np.average(left_data, axis=0)) / 3
  174. # for j in range(rest_data.shape[0]):
  175. # rest_data[j, i, k] = rest_data[j, i, k] / avg[i, k]
  176. # for j in range(left_data.shape[0]):
  177. # left_data[j, i, k] = left_data[j, i, k] / avg[i, k]
  178. # for j in range(right_data.shape[0]):
  179. # right_data[j, i, k] = right_data[j, i, k] / avg[i, k]
  182. # for i in range(rest_data.shape[2]):
  183. # rest_data[:, :, i] = (rest_data[:, :, i] - np.min(rest_data[:, :, i])) / np.std(rest_data[:, :, i])
  184. # left_data[:, :, i] = (left_data[:, :, i] - np.min(left_data[:, :, i])) / np.std(left_data[:, :, i])
  185. # right_data[:, :, i] = (right_data[:, :, i] - np.min(right_data[:, :, i])) / np.std(right_data[:, :, i])
  187. for i in range(rest_data.shape[2]):
  188. rest_data[:, :, i] = (rest_data[:, :, i] - np.min(rest_data[:, :, i])) / (np.max(rest_data[:, :, i]) - np.min(rest_data[:, :, i]))
  189. left_data[:, :, i] = (left_data[:, :, i] - np.min(left_data[:, :, i])) / (np.max(left_data[:, :, i]) - np.min(left_data[:, :, i]))
  190. right_data[:, :, i] = (right_data[:, :, i] - np.min(right_data[:, :, i])) / (np.max(right_data[:, :, i]) - np.min(right_data[:, :, i]))
  192. rest_average = rest_data.mean(axis=0)
  193. left_average = left_data.mean(axis=0)
  194. right_average = right_data.mean(axis=0)
  196. print(rest_data.shape)
  197. print(left_data.shape)
  198. print(right_data.shape)
  200. shuffling_vector_rest = np.arange((rest_data.shape[0]))
  201. shuffling_vector_left = np.arange((left_data.shape[0]))
  202. shuffling_vector_right = np.arange((right_data.shape[0]))
  203. np.random.shuffle(shuffling_vector_rest)
  204. np.random.shuffle(shuffling_vector_left)
  205. np.random.shuffle(shuffling_vector_right)
  207. rest_data = rest_data[shuffling_vector_rest, :, :]
  208. left_data = left_data[shuffling_vector_left, :, :]
  209. right_data = right_data[shuffling_vector_right, :, :]
  211. fig, (ax1, ax2, ax3) = plt.subplots(1, 3, sharey=True)
  212. ax1.plot(rest_average)
  213. ax2.plot(left_average)
  214. ax3.plot(right_average)
  215. plt.show()
  217. training_split = 0.8
  218. training_data = np.concatenate((rest_data[0:int(np.round(training_split * rest_data.shape[0])), :, :],
  219. left_data[0:int(np.round(training_split * left_data.shape[0])), :, :],
  220. right_data[0:int(np.round(training_split * right_data.shape[0])), :, :]), axis=0)
  221. test_data = np.concatenate((rest_data[int(np.round(training_split * rest_data.shape[0])):rest_data.shape[0], :, :],
  222. left_data[int(np.round(training_split * left_data.shape[0])):left_data.shape[0], :, :],
  223. right_data[int(np.round(training_split * right_data.shape[0])):right_data.shape[0], :, :]), axis=0)
  225. avg = np.average(np.concatenate((training_data, test_data), axis=0))
  226. std = np.std(np.concatenate((training_data, test_data), axis=0))
  227. training_data = (training_data - avg) / std
  228. test_data = (test_data - avg) / std
  230. training_data2 = np.reshape(training_data, (training_data.shape[0], training_data.shape[1] * training_data.shape[2]))
  231. test_data2 = np.reshape(test_data, (test_data.shape[0], test_data.shape[1] * test_data.shape[2]))
  232. training_labels = np.zeros((training_data.shape[0]), dtype=int)
  233. training_labels[int(np.round(training_split * rest_data.shape[0])):int(np.round(training_split * rest_data.shape[0])) + int(np.round(training_split * left_data.shape[0]))] = 1
  234. training_labels[int(np.round(training_split * rest_data.shape[0])) + int(np.round(training_split * left_data.shape[0])):training_labels.shape[0]] = 2
  235. test_labels = np.zeros((test_data.shape[0]), dtype=int)
  236. test_labels[int(np.round((1 - training_split) * rest_data.shape[0])):int(np.round((1 - training_split) * rest_data.shape[0])) + int(np.round((1 - training_split) * left_data.shape[0]))] = 1
  237. test_labels[int(np.round((1 - training_split) * rest_data.shape[0])) + int(np.round((1 - training_split) * left_data.shape[0])):test_labels.shape[0]] = 2
  239. clf = svm.NuSVC(nu=0.1, kernel='rbf', class_weight='balanced', decision_function_shape='ovo', tol=1e-3, verbose=True)
  240. clf.fit(training_data2, training_labels)
  241. test_labels_predicted = clf.predict(test_data2)
  242. cm = confusion_matrix(test_labels, test_labels_predicted)
  243. print(cm)
  244. print(accuracy_score(test_labels, test_labels_predicted))
  246. clf = RandomForestClassifier(n_estimators=100, criterion='entropy')
  247. clf.fit(training_data2, training_labels)
  248. test_labels_predicted = clf.predict(test_data2)
  249. cm = confusion_matrix(test_labels, test_labels_predicted)
  250. print(cm)
  251. print(accuracy_score(test_labels, test_labels_predicted))
  253. training_data = training_data.reshape((training_data.shape[0], training_data.shape[1], training_data.shape[2], 1))
  254. test_data = test_data.reshape((test_data.shape[0], test_data.shape[1], test_data.shape[2], 1))
  255. training_labels_CNN = keras.utils.to_categorical(training_labels, 3)
  256. test_labels_CNN = keras.utils.to_categorical(test_labels, 3)
  258. model = keras.Sequential(
  259. [
  260. keras.Input(shape=training_data[0].shape),
  261. layers.Conv2D(8, kernel_size=(3, 3), activation="relu", padding="same"),
  262. layers.Dropout(0.5),
  263. layers.Conv2D(16, kernel_size=(3, 3), activation="relu", padding="same"),
  264. layers.Dropout(0.5),
  265. layers.MaxPooling2D(pool_size=(2, 2)),
  266. layers.Flatten(),
  267. layers.Dropout(0.5),
  268. layers.Dense(3, activation="softmax"),
  269. ]
  270. )
  272. batch_size = training_data.shape[0]
  273. epochs = 2500
  275. model.compile(loss="categorical_crossentropy", optimizer="adam", metrics=["accuracy"])
  276. callback = keras.callbacks.EarlyStopping(monitor='val_accuracy', patience=1000, start_from_epoch=25, restore_best_weights=True)
  277. train_history = model.fit(training_data, training_labels_CNN, batch_size=batch_size, epochs=epochs, callbacks=callback, validation_split=0.25)
  278. test_labels_predicted = np.argmax(model.predict(test_data), axis=1)
  279. cm = confusion_matrix(test_labels, test_labels_predicted)
  280. print(cm)
  281. score = model.evaluate(test_data, test_labels_CNN, verbose=0)
  282. print("Test loss:", score[0])
  283. print("Test accuracy:", score[1])
  285. loss = train_history.history['loss']
  286. accuracy = train_history.history['accuracy']
  287. val_loss = train_history.history['val_loss']
  288. val_accuracy = train_history.history['val_accuracy']
  289. plt.plot(loss)
  290. plt.plot(accuracy)
  291. plt.plot(val_loss)
  292. plt.plot(val_accuracy)
  293. plt.legend(['loss', 'accuracy', 'val_loss', 'val_accuracy'])
  294. plt.show()

MI.py at commit fef74d6, no license · at the source

Overview

Authors: János Rokai1, Melinda Rácz1,2,3, Melinda Becske2,3,4,5, János Csipor6, Csaba Márton Köllőd1,7, István Ulbert1,7,8, Gergely Márton1,6
  1. Institute of Cognitive Neuroscience and Psychology, HUN-REN Research Centre for Natural Sciences, Magyar Tudósok krt. 2, Budapest, 1117 Hungary
  2. School of PhD Studies, Semmelweis University, Üllői út 26, Budapest, 1085 Hungary
  3. Selye János Doctoral College for Advanced Studies, Semmelweis University, Üllői út 22, Budapest, 1085 Hungary
  4. Department of Psychiatry and Psychotherapy, Semmelweis University, Balassa u. 6, Budapest, 1083 Hungary
  5. Nyiro Gyula National Institute of Psychiatry and Addictology, Neurocognitive Research Centre, 59-61 Lehel Utca, Budapest, 1135 Hungary
  6. MindRove Kft., Hédervári út 43, Gyor, 9026 Hungary
  7. Faculty of Information Technology and Bionics, Pázmány Péter Catholic University, Práter Utca 50/a, Budapest, 1083 Hungary
  8. Department of Neurosurgery and Neurointervention, Faculty of Medicine, Semmelweis University, Amerikai út 57, Budapest, 1145 Hungary
Institutions: Institute of Cognitive Neuroscience and Psychology (Hungary); HUN-REN Research Centre for Natural Sciences (Hungary); Semmelweis University (Hungary); Pázmány Péter Catholic University (Hungary)
Journal: Scientific reports, volume 16, issue 1, article 23147
Dates: received 19 January 2026; accepted 6 May 2026; published online 24 July 2026
Type: Research article · Language: English
License: CC BY
Identifiers: DOI 10.1038/s41598-026-52672-8 · PMID 42493516 · PMCID PMC13396441 · OpenAlex W7170172014
Open access: gold, a free copy (OpenAlex)
Status: code verified
Categories: EEG (modality), human (organism), methods / tools (subfield)
Methods: Spectral & time-frequency, Statistics, Smoothing, state filtering, decompositions, Machine learning, Preprocessing, Evoked potentials, Physiology & signal measures
Keywords: Electroencephalography, Portable electroencephalography device, Visually evoked potential, Transient visually evoked potential, Event-related potential, P300, Motor execution, Brain–computer interface, Biological techniques, Engineering, Neuroscience
MeSH: Brain-Computer Interfaces*, Electroencephalography*, Convolutional Neural Networks, Electrodes, Event-Related Potentials, P300, Evoked Potentials, Visual, Humans, Signal-To-Noise Ratio, Support Vector Machine (* major topic)
Topic: EEG and Brain-Computer Interfaces (Cognitive Neuroscience, Neuroscience), according to OpenAlex
Funding: HUN-REN Research Centre for Natural Sciences
Citations: cited by 1 paper (Europe PMC); 101 references in the paper

Abstract

In recent years, commercial lightweight electroencephalography (EEG) headsets are gaining popularity in neuroscience. These devices commonly utilize only a few dry electrodes in specific locations and signal quality is often inferior compared to that of their traditional counterparts. In this study, we wanted to assess the feasibility of portable, paste-less, passive electrode-based EEG headset MindRove vision (VSN) for laboratory use. Three paradigms were implemented for acquiring visual evoked potential (VEP), P300 event-related potential and motor execution task (ME) related cortical patterns. Measurements were taken by using VSN, with wet-electrode system mBrainTrain SMARTING applied as reference. The performance of the devices was assessed by using signal-to-noise ratio (SNR) for VEP and P300 while support vector machine, random forest and convolutional neural network-based classifiers were fit to ME data. The SNRdB (i.e. SNR expressed in decibels) of VSN was greater for both VEP and P300, by a margin of 1.998 and 2.845 dB, respectively. There was a significant difference between VEP signal amplitude levels and SNRdB, P300 SNR and SNRdB in favor of VSN. Average accuracy of the sorters were 78.8% for VSN and 80.9% for SMARTING; the difference was not significant. The application of VSN is feasible for use in research besides qualitative exploration.

Reproduced under the paper's license (CC BY), from the paper cited above.

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MelindaRacz/VSN_validation_supplementary

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State: the link answers, verified on 27 September 2026
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Commit: fef74d666f998999839cddc889d90e15251c7b12, 6 September 2024
Languages: R (8), Python (3)
Size: 317 files, 11 scripts
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Found in: “Data availability”
Holds: README, 8 notebooks
Not found: license file, CITATION.cff, environment file, tests, continuous integration, documentation
Tools: Matplotlib (3 files), NumPy (3 files), scikit-learn (3 files), SciPy (3 files), Keras (1 file)
Availability: 1 check, the latest on 27 September 2026: the link answers
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12 files
At the source: github.com/MelindaRacz/VSN_validation_supplementary

The paper's code and data availability statement is in the Data section.

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Data

Datasets cited

Data availability

Measurement data, preprocessing scripts, additional figures and details on statistical analysis are provided as supplementary material to this article, made accessible at (https://github.com/MelindaRacz/VSN_validation_supplementary). Other details are provided upon request addressed to the corresponding author.

Reproduced under the paper's license (CC BY), from the paper cited above.

Versions

The history of this record: each version stored by the harvester or made by a correction of its authors or of the maintainers of its code, and what changed in its facts. The texts of the paper (its abstract, its availability statements) are not part of it; versions that changed only those are not listed.

Version 1, 27 September 2026: the first record

Recorded: type, language, journal, volume, issue, pages, dates, 7 authors, 11 keywords, 9 MeSH terms, 1 funder, 72 references.

Cite

This paper

Rokai, J., Rácz, M., Becske, M., Csipor, J., Köllőd, C. M., Ulbert, I., & Márton, G. (2026). Validation of portable, semi-dry electrode-based electroencephalography device for its application in brain-computer interface solutions. Scientific reports, 16(1), 23147. https://doi.org/10.1038/s41598-026-52672-8

BibTeX

@article{rokai2026validation,
author = {Rokai, János and Rácz, Melinda and Becske, Melinda and Csipor, János and Köllőd, Csaba Márton and Ulbert, István and Márton, Gergely},
title = {{Validation of portable, semi-dry electrode-based electroencephalography device for its application in brain-computer interface solutions}},
journal = {Scientific reports},
year = {2026},
month = jul,
volume = {16},
number = {1},
pages = {23147},
publisher = {Nature Publishing Group},
issn = {2045-2322},
doi = {10.1038/s41598-026-52672-8},
url = {https://doi.org/10.1038/s41598-026-52672-8},
pmid = {42493516},
pmcid = {PMC13396441}
}

RIS

TY - JOUR
AU - Rokai, János
AU - Rácz, Melinda
AU - Becske, Melinda
AU - Csipor, János
AU - Köllőd, Csaba Márton
AU - Ulbert, István
AU - Márton, Gergely
TI - Validation of portable, semi-dry electrode-based electroencephalography device for its application in brain-computer interface solutions
T2 - Scientific reports
J2 - Sci Rep
PY - 2026
DA - 2026/07/24
VL - 16
IS - 1
SP - 23147
SN - 2045-2322
PB - Nature Publishing Group
DO - 10.1038/s41598-026-52672-8
UR - https://doi.org/10.1038/s41598-026-52672-8
LA - en
ER -

CSL-JSON

{
"id": "10.1038/s41598-026-52672-8",
"type": "article-journal",
"title": "Validation of portable, semi-dry electrode-based electroencephalography device for its application in brain-computer interface solutions",
"container-title": "Scientific reports",
"author": [
{
"family": "Rokai",
"given": "János"
},
{
"family": "Rácz",
"given": "Melinda"
},
{
"family": "Becske",
"given": "Melinda"
},
{
"family": "Csipor",
"given": "János"
},
{
"family": "Köllőd",
"given": "Csaba Márton"
},
{
"family": "Ulbert",
"given": "István"
},
{
"family": "Márton",
"given": "Gergely"
}
],
"container-title-short": "Sci Rep",
"volume": "16",
"issue": "1",
"page": "23147",
"DOI": "10.1038/s41598-026-52672-8",
"PMID": "42493516",
"PMCID": "PMC13396441",
"ISSN": "2045-2322",
"publisher": "Nature Publishing Group",
"URL": "https://doi.org/10.1038/s41598-026-52672-8",
"language": "en",
"issued": {
"date-parts": [
[
2026,
7,
24
]
]
}
}

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