torcheeg.io

EEGSignalIO

class torcheeg.io.EEGSignalIO(cache_path: str, cache_size: int = 1099511627776)[source]

Bases: object

A general-purpose, lightweight and efficient EEG signal IO APIs for converting various real-world EEG signal datasets into np.ndarray samples and storing them in the database. Here, we draw on the implementation ideas of industrial-grade application Caffe, and encapsulate a set of EEG signal reading and writing methods based on Lightning Memory-Mapped Database (LMDB), which not only unifies the differences of data types in different databases, but also accelerates the reading of data during training and testing.

eeg_io = EEGSignalIO('YOUR_PATH')
key = eeg_io.write_eeg(np.random.randn(32, 128))
eeg = eeg_io.read_eeg(key)
eeg.shape
>>> (32, 128)

Parameters

cache_path (str) – Where the database is stored.
cache_size (int) – The maximum capacity of the database. (default: 1099511627776)

get_eeg_format() → Tuple[ndarray, str][source]

Get the sample data shapes and types acceptable to the database.

Returns: the sample data shapes and types acceptable to the database.
Return type: tuple (np.ndarray, str)

read_eeg(key: str) → ndarray[source]

Query the corresponding EEG signal in the database according to the index.

Parameters: key (str) – The index of the EEG signal to be queried.
Returns: The EEG signal sample.
Return type: np.ndarray

set_eeg_format(eeg: ndarray) → None[source]

Set the sample data shapes and types acceptable to the database based on the inserted EEG signals.

Parameters: eeg (np.ndarray) – The EEG signal sample to be written into the database.

write_eeg(eeg: ndarray, key: Optional[str] = None) → str[source]

Write EEG signal to database.

Parameters

eeg (np.ndarray) – EEG signal samples to be written into the database.
key (str, optional) – The key of the EEG signal to be inserted, if not specified, it will be an auto-incrementing integer.

Returns

The index of written EEG signals in the database.

Return type

int

property write_pointer

MetaInfoIO

class torcheeg.io.MetaInfoIO(cache_path: str)[source]

Bases: object

Use with torcheeg.io.EEGSignalIO to store description information for EEG signals in the form of a table, so that the user can still analyze, insert, delete and modify the corresponding information after the generation is completed.

info_io = MetaInfoIO('YOUR_PATH')
key = info_io.write_info({
    'clip_id': 0,
    'baseline_id': 1,
    'valence': 1.0,
    'arousal': 9.0
})
info = info_io.read_info(key).to_dict()
>>> {
        'clip_id': 0,
        'baseline_id': 1,
        'valence': 1.0,
        'arousal': 9.0
    }

Parameters: cache_path (str) – Where the table is stored.

read_all() → DataFrame[source]

Get all EEG descriptions in the database in tabular form.

Returns: The EEG descriptions.
Return type: pd.DataFrame

read_info(key) → DataFrame[source]

Query the corresponding EEG description in the table according to the index.

Parameters: key (int) – The index of the EEG description to be queried.
Returns: The EEG description.
Return type: pd.DataFrame

write_info(obj: Dict) → int[source]

Insert a description of the EEG signal.

Parameters: obj (dict) – The description to be written into the table.
Returns: The index of written EEG description in the table.
Return type: int