EEG Alpha Wave Recording by Kaggle | Healthcare

About this Dataset

EEG Alpha Wave Recording

Investigating Brain Activity in a Resting-State Experiment

About this dataset

This dataset presents a comprehensive resource for electroencephalographic (EEG) analysis. It contains sixteen (16) recordings of alpha wave activity from twenty (20) individuals who took part in a 2017 resting-state experiment. The recordings were taken using an EEG during the experiment protocol when participants had their eyes open/closed. This valuable resource will enable professionals to gain meaningful insights into brain wave patterns and the measurements of different parts of the brain. Each row represents one electrode recording, and the columns are indexed numerically, providing data points that can be used to interpret complex neurological phenomena in greater detail. This dataset offers an unprecedented opportunity to have access to high-quality EEG data that can facilitate both research into neural processes and indications of neurological disorders

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How to use the dataset

In order to use this dataset, users must first download the data files from Kaggle. The data is stored in comma separated value (CSV) format on each file and contains 42 columns representing the subject ID, 16 different electrode recordings from the left or right side of each brain lobe (frontal, temporal, parietal, occipital) or junctional area (frontal-central), plus 20 additional alpha wave readings.

Once downloaded onto your preferred system software package or platform such as Python/R/Jupyter notebook/Matlab etc., users can then start to perform some analysis on these datasets which typically involve loading their data into an appropriate container such as a DataFrame structure if they are using Python Pandas modules to manipulate their data. They should also check that all column features are well subjected before proceeding any further by plotting some graphs based off those features provided for a particular test subject over time - (e.g plotting Alpha waves measurements concurrently with corresponding electrode readings).

From there onwards users may invoke machine learning models using either statistical methods or AI algorithms depending on their requirement objectives - emphasizing within supervised ML contexts here where you're applying it to classify distinct patterns within EEG signals generated by multiple participants inside this datasets; Unsupervised ones may be used when trying out clustering applications that would help give better insights into EEG signals comparisons between different participants at rest; Reinforcement learning can be applied to simulate scenarios whereby user wishes to diagnose certain mental conditions among others associated with undesired behaviors being signaled through varying EEG activities recorded over small intervals of time exposed amongst participating subjects tested inside this dataset scenario's respectively

Additional more advanced forms of analysis would involve various filtering techniques being employed as part of signal processing operations towards segmenting out undesirable frequencies among complete range spectrums founds within raw electroencephalography signals alongside additional methodological approaches such as Spectroclygrams decompostions etc…to emit maximized spectral power interference therein at desired bands enhancing signal detectability improving accuracy amongst other benefits associated with its effectiveness

Lastly cross correlation processes may also come up inductively useful when prescribed during interpretations performed against multiple sources involving EEG signal chronologies since it’s motioned primarily

Research Ideas

Analyzing Alpha wave activity across diverse brain regions in the resting-state experiment to identify cognitive processes associated with changes in brain electrical activity;

Performing predictive analysis on EEG signals to predict disease progression, risk or changes associated with psychological stress;

Applying Machine-Learning/Artificial Intelligence approaches to compare and classify electroencephalogram recordings from multiple participants for diagnosing neurological diseases and suggesting personalized therapies based on the patient's EEG data

Acknowledgements

If you use this dataset in your research, please credit the original authors.
Data Source

License

License: CC0 1.0 Universal (CC0 1.0) - Public Domain Dedication
No Copyright - You can copy, modify, distribute and perform the work, even for commercial purposes, all without asking permission. See Other Information.

Columns

File: subject_09.csv

Column name	Description
0	Subject ID (Numeric)
-6260.3	Left Parietal Lobe (Numeric)
14060	Right Parietal Lobe (Numeric)
9934.1	Left Frontal Lobe (Numeric)
6478.4	Right Frontal Lobe (Numeric)
3929	Left Temporal Lobe (Numeric)
3740.7	Right Temporal Lobe (Numeric)
2781.8	Left Occipital Lobe (Numeric)
152.39	Right Occipital Lobe (Numeric)
3142.8	Left Central Lobe (Numeric)
6607.8	Right Central Lobe (Numeric)
-1854.3	Left Frontal Midline (Numeric)
12934	Right Frontal Midline (Numeric)
8224.9	Left Parietal Midline (Numeric)
14637	Right Parietal Midline (Numeric)
9547.2	Left Temporal Midline (Numeric)
13133	Right Temporal Midline (Numeric)
0.1	Alpha Wave Recording (Numeric)
0.2	Alpha Wave Recording (Numeric)

File: subject_15.csv

Column name	Description
0	Subject ID (Numeric)
0.1	Alpha Wave Recording (Numeric)
0.2	Alpha Wave Recording (Numeric)
650.97	Electrode recording from the left parietal lobe (Numeric)
13135	Electrode recording from the right frontal lobe (Numeric)
4109.8	Electrode recording from the left temporal lobe (Numeric)
-3475.2	Electrode recording from the right temporal lobe (Numeric)
7822.9	Electrode recording from the left occipital lobe (Numeric)
10022	Electrode recording from the right occipital lobe (Numeric)
9119.3	Electrode recording from the left frontal lobe (Numeric)
-11472	Electrode recording from the right parietal lobe (Numeric)
16687	Electrode recording from the left temporal-parietal junction (Numeric)
5203	Electrode recording from the right temporal-parietal junction (Numeric)
8366.5	Electrode recording from the left frontal-temporal junction (Numeric)
12046	Electrode recording from the right frontal-temporal junction (Numeric)
-2366.4	Electrode recording from the left occipital-temporal junction (Numeric)
8140.3	Electrode recording from the right occipital-temporal junction (Numeric)
4863.9	Electrode recording from the left frontal-parietal junction (Numeric)
-1161.9	Electrode recording from the right frontal-parietal junction (Numeric)

Acknowledgements

If you use this dataset in your research, please credit the original authors.
If you use this dataset in your research, please credit .

Tables

Demographic

@kaggle.thedevastator_eeg_alpha_wave_recording.demographic

3.47 KB
19 rows
4 columns


CREATE TABLE demographic (
  "n_1" BIGINT,
  "n_1_1" BIGINT,
  "n_27" BIGINT,
  "n_5" BIGINT
);

Demographic Header

@kaggle.thedevastator_eeg_alpha_wave_recording.demographic_header

2.58 KB
4 columns


CREATE TABLE demographic_header (
  "subject" VARCHAR,
  "isman" VARCHAR,
  "age" VARCHAR,
  "fatigue" VARCHAR
);

Header

@kaggle.thedevastator_eeg_alpha_wave_recording.header

8.57 KB
19 columns


CREATE TABLE header (
  "time" VARCHAR,
  "fp1" VARCHAR,
  "fp2" VARCHAR,
  "fc5" VARCHAR,
  "fz" VARCHAR,
  "fc6" VARCHAR,
  "t7" VARCHAR,
  "cz" VARCHAR,
  "t8" VARCHAR,
  "p7" VARCHAR,
  "p3" VARCHAR,
  "pz" VARCHAR,
  "p4" VARCHAR,
  "p8" VARCHAR,
  "o1" VARCHAR,
  "oz" VARCHAR,
  "o2" VARCHAR,
  "eyesclosed" VARCHAR,
  "eyesopened" VARCHAR
);

Subject 01

@kaggle.thedevastator_eeg_alpha_wave_recording.subject_01

5.1 MB
119807 rows
19 columns


CREATE TABLE subject_01 (
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Subject 02

@kaggle.thedevastator_eeg_alpha_wave_recording.subject_02

5.29 MB
183647 rows
19 columns


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Subject 03

@kaggle.thedevastator_eeg_alpha_wave_recording.subject_03

2.47 MB
91071 rows
19 columns


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Subject 04

@kaggle.thedevastator_eeg_alpha_wave_recording.subject_04

3.72 MB
97695 rows
19 columns


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Subject 05

@kaggle.thedevastator_eeg_alpha_wave_recording.subject_05

2.91 MB
95135 rows
19 columns


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Subject 06

@kaggle.thedevastator_eeg_alpha_wave_recording.subject_06

2.97 MB
97119 rows
19 columns


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Subject 07

@kaggle.thedevastator_eeg_alpha_wave_recording.subject_07

2.42 MB
63103 rows
19 columns


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Subject 08

@kaggle.thedevastator_eeg_alpha_wave_recording.subject_08

2.99 MB
78815 rows
19 columns


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Subject 09

@kaggle.thedevastator_eeg_alpha_wave_recording.subject_09

2.56 MB
74111 rows
19 columns


CREATE TABLE subject_09 (
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Subject 10

@kaggle.thedevastator_eeg_alpha_wave_recording.subject_10

2.98 MB
79263 rows
19 columns


CREATE TABLE subject_10 (
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Subject 11

@kaggle.thedevastator_eeg_alpha_wave_recording.subject_11

2.39 MB
89439 rows
19 columns


CREATE TABLE subject_11 (
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Subject 12

@kaggle.thedevastator_eeg_alpha_wave_recording.subject_12

2.58 MB
87519 rows
19 columns


CREATE TABLE subject_12 (
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);

Subject 13

@kaggle.thedevastator_eeg_alpha_wave_recording.subject_13

2.76 MB
85535 rows
19 columns


CREATE TABLE subject_13 (
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Subject 14

@kaggle.thedevastator_eeg_alpha_wave_recording.subject_14

2.84 MB
77503 rows
19 columns


CREATE TABLE subject_14 (
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Subject 15

@kaggle.thedevastator_eeg_alpha_wave_recording.subject_15

2.85 MB
77727 rows
19 columns


CREATE TABLE subject_15 (
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);

Subject 16

@kaggle.thedevastator_eeg_alpha_wave_recording.subject_16

3.29 MB
91423 rows
19 columns


CREATE TABLE subject_16 (
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);

Subject 17

@kaggle.thedevastator_eeg_alpha_wave_recording.subject_17

3.32 MB
84639 rows
19 columns


CREATE TABLE subject_17 (
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);

Subject 18

@kaggle.thedevastator_eeg_alpha_wave_recording.subject_18

3.69 MB
89823 rows
19 columns


CREATE TABLE subject_18 (
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  "n_0_1" BIGINT,
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);

Subject 19

@kaggle.thedevastator_eeg_alpha_wave_recording.subject_19

1.59 MB
90911 rows
19 columns


CREATE TABLE subject_19 (
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  "n_0_1" BIGINT,
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);

Subject 20

@kaggle.thedevastator_eeg_alpha_wave_recording.subject_20

2.93 MB
81759 rows
19 columns


CREATE TABLE subject_20 (
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