Forecasting Eye State from Electroencephalogram Readings with MindsDB

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Introduction

In this tutorial, we’ll create and train a machine learning model, or as we call it, an AI Table or a predictor. By querying the model, we’ll produce categorical forecasts for a multivariate time series.

Install MindsDB locally via Docker or Docker Desktop.

Let’s get started.

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Data Setup

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Connecting the Data

There are a couple of ways you can get the data to follow through with this tutorial.

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Connecting as a database

You can connect to a demo database that we’ve prepared for you. It contains the data used throughout this tutorial (the example_db.demo_data.eeg_eye table).

CREATE DATABASE example_db
    WITH ENGINE = "postgres",
    PARAMETERS = {
        "user": "demo_user",
        "password": "demo_password",
        "host": "samples.mindsdb.com",
        "port": "5432",
        "database": "demo"
};

Now you can run queries directly on the demo database. Let’s preview the data that we’ll use to train our predictor.

SELECT *
FROM example_db.demo_data.eeg_eye
LIMIT 10;

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Connecting as a file

The dataset we use in this tutorial is the UCI’s EEG Eye State dataset. You can download it here in the ARFF format that should be converted to the CSV format before uploading it via MindsDB SQL Editor.

Follow this guide to find out how to upload a file to MindsDB.

Now you can run queries directly on the file as if it were a table. Let’s preview the data that we’ll use to train our predictor.

SELECT *
FROM files.eeg_eye
LIMIT 10;

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Understanding the Data

We use the UCI’s EEG Eye State dataset, where each row contains data of one electroencephalogram (EEG) reading plus the current state of the patient’s eye, where 0 indicates open eye and 1 indicates closed eye. We want to know ahead of time when the eye state will change, so we predict the eyeDetection column.

Below is the sample data stored in the files.eeg_eye table.

+----------+----------+----------+----------+----------+----------+----------+----------+----------+----------+----------+----------+----------+----------+--------------+
| AF3      | F7       | F3       | FC5      | T7       | P7       | O1       | O2       | P8       | T8       | FC6      | F4       | F8       | AF4      | eyeDetection |
+----------+----------+----------+----------+----------+----------+----------+----------+----------+----------+----------+----------+----------+----------+--------------+
| 4329.23  | 4009.23  | 4289.23  | 4148.21  | 4350.26  | 4586.15  | 4096.92  | 4641.03  | 4222.05  | 4238.46  | 4211.28  | 4280.51  | 4635.9   | 4393.85  | 0            |
| 4324.62  | 4004.62  | 4293.85  | 4148.72  | 4342.05  | 4586.67  | 4097.44  | 4638.97  | 4210.77  | 4226.67  | 4207.69  | 4279.49  | 4632.82  | 4384.1   | 0            |
| 4327.69  | 4006.67  | 4295.38  | 4156.41  | 4336.92  | 4583.59  | 4096.92  | 4630.26  | 4207.69  | 4222.05  | 4206.67  | 4282.05  | 4628.72  | 4389.23  | 0            |
| 4328.72  | 4011.79  | 4296.41  | 4155.9   | 4343.59  | 4582.56  | 4097.44  | 4630.77  | 4217.44  | 4235.38  | 4210.77  | 4287.69  | 4632.31  | 4396.41  | 0            |
| 4326.15  | 4011.79  | 4292.31  | 4151.28  | 4347.69  | 4586.67  | 4095.9   | 4627.69  | 4210.77  | 4244.1   | 4212.82  | 4288.21  | 4632.82  | 4398.46  | 0            |
+----------+----------+----------+----------+----------+----------+----------+----------+----------+----------+----------+----------+----------+----------+--------------+

Where:

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Training a Predictor

Let’s create and train the machine learning model. For that, we use the CREATE MODEL statement and specify the input columns used to train FROM (features) and what we want to PREDICT (labels).

The eyeDetection column is our target variable. The interesting thing about this example is that we aim to forecast labels that are not strictly numerical. Even though this example is simple (because the variable is a binary category), this can easily be generalized to more than two categories.

We order the measurements by the Timestamps column that shows readings frequency of approximately 8 milliseconds.

CREATE MODEL mindsdb.eeg_eye_forecast
FROM example_db
  (SELECT * FROM demo_data.eeg_eye)
PREDICT eyedetection
ORDER BY timestamps
WINDOW 50
HORIZON 10;

As the sampling frequency is 8 ms, this predictor is trained using a historical context of roughly 400 ms ((50 * 8) = 400 [ms]) to predict the following 80 ms ((10 * 8) = 80 [ms]).

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Status of a Predictor

A predictor may take a couple of minutes for the training to complete. You can monitor the status of the predictor by using this SQL command:

DESCRIBE eeg_eye_forecast;

If we run it right after creating a predictor, we get this output:

+------------+
| status     |
+------------+
| generating |
+------------+

A bit later, this is the output:

+----------+
| status   |
+----------+
| training |
+----------+

And at last, this should be the output:

+----------+
| status   |
+----------+
| complete |
+----------+

Now, if the status of our predictor says complete, we can start making predictions!

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Making Predictions

You can make predictions by querying the predictor joined with the data table. The SELECT statement lets you make predictions for the label based on the chosen features for a given time period. Usually, you want to know what happens right after the latest training data point that was fed. We have a special keyword for that, the LATEST keyword.

Let’s run a query to get predictions for the next HORIZON timesteps into the future, which in this case is roughly 80 milliseconds.

SELECT m.timestamps, m.eyedetection
FROM example_db.demo_data.eeg_eye as t
JOIN mindsdb.eeg_eye_forecast as m
WHERE t.timestamps > LATEST
LIMIT 10;

On execution, we get:

+----------------------------+--------------+
| timestamps                 | eyedetection |
+----------------------------+--------------+
| 2001-09-03 08:01:57.000000 | 1            |
| 2001-09-03 08:01:57.008000 | 1            |
| 2001-09-03 08:01:57.016000 | 1            |
| 2001-09-03 08:01:57.024000 | 1            |
| 2001-09-03 08:01:57.032000 | 1            |
| 2001-09-03 08:01:57.040000 | 1            |
| 2001-09-03 08:01:57.048000 | 1            |
| 2001-09-03 08:01:57.056000 | 1            |
| 2001-09-03 08:01:57.064000 | 1            |
| 2001-09-03 08:01:57.072000 | 1            |
+----------------------------+--------------+

That’s it. We can now JOIN any set of WINDOW rows worth of measurements with this predictor, and forecasts will be emitted to help us expect a change in the state of the patient’s eye based on the EEG readings.

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Alternate Problem Framings

It is also possible to reframe this task as a normal forecasting scenario where the variable is numeric. There are a few options here. It boils down to what the broader scenario is and what format would maximize the value of any specific prediction. For example, a simple mapping of eye is open to 0 and eye is closed to 1 would be enough to replicate the above behavior. We could also explore other options. With some data transformations on the data layer, we could get a countdown to the next change in state, effectively predicting a date if we cast this back into the timestamp domain.

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What’s Next?

Have fun while trying it out yourself!

• Bookmark MindsDB repository on GitHub.
• Install MindsDB locally via Docker or Docker Desktop.
• Engage with the MindsDB community on Slack or GitHub to ask questions and share your ideas and thoughts.

If this tutorial was helpful, please give us a GitHub star here.