mixnet.preprocessing.OpenBMI
View source on GitHubTable of contents
- mixnet.preprocessing.OpenBMI
Time domain
Subject-dependent setting
Preprocess raw time-series EEG in subject-dependent setting using butter bandpass filter and resampling. Split data into train, validation and test sets using stratified k-fold cross-validation.
time_domain.subject_dependent(k_folds,
pick_smp_freq,
bands,
order,
save_path,
num_class,
sel_chs)
Arguments:
| Arguments | Description |
|---|---|
| k_folds | int number of k-fold cross-validation. |
| pick_smp_freq | int pick sample frequency (downsampling EEG to to pick_smp_freq) |
| bands | list list of low cut and high cut frequency bands e.g. [8, 30] |
| order | int number of order of butter bandpass filter |
| save_path | str path to save processed EEG |
| num_class | int number of classes. Default 2 |
| sel_chs | list or None. list if EEG channels. Default None |
Example
import mixnet.preprocessing as prep
prep.OpenBMI.time_domain.subject_dependent_setting(k_folds=5,
pick_smp_freq=100,
bands=[8, 30],
order=5,
save_path='datasets')
Subject-independent setting
Preprocess raw time-series EEG in subject-independent setting using butter bandpass filter and resampling. Split data into train, validation and test sets using stratified k-fold cross-validation.
time_domain.subject_independent(k_folds,
pick_smp_freq,
bands,
order,
save_path,
num_class,
sel_chs)
Arguments:
| Arguments | Description |
|---|---|
| k_folds | int number of k-fold cross-validation. |
| pick_smp_freq | int pick sample frequency (downsampling EEG to to pick_smp_freq) |
| bands | list list of low cut and high cut frequency bands e.g. [8, 30] |
| order | int number of order of butter bandpass filter |
| save_path | str path to save processed EEG |
| num_class | int number of classes. Default 2 |
| sel_chs | list or None. list if EEG channels. Default None |
Example
import mixnet.preprocessing as prep
prep.OpenBMI.time_domain.subject_independent_setting(k_folds=5,
pick_smp_freq=100,
bands=[8, 30],
order=5,
save_path='datasets')
Filter Bank Common Spatial Pattern (FBCSP)
Subject-dependent setting
Preprocess raw time-series EEG in subject-dependent setting using Filter Bank Common Spatial Pattern (FBCSP). Split data into train, validation and test sets using stratified k-fold cross-validation.
fbcsp.subject_dependent(k_folds,
pick_smp_freq,
n_components,
bands,
n_features,
order,
save_path,
num_class,
sel_chs)
Arguments:
| Arguments | Description |
|---|---|
| k_folds | int number of k-fold cross-validation. |
| pick_smp_freq | int pick sample frequency (downsampling EEG to to pick_smp_freq) |
| n_components | int number of components of CSP |
| bands | list list of low cut and high cut frequency bands of filter bank e.g. [[4, 8], [8, 12], ...] |
| n_features | int number of features for mutual_info_classif |
| order | int number of order of butter bandpass filter |
| save_path | str path to save processed EEG |
| num_class | int number of classes. Default 2 |
| sel_chs | list or None. list if EEG channels. Default None |
Example
import mixnet.preprocessing as prep
bands = [[4, 8], [8, 12], [12, 16],
[16, 20], [20, 24], [24, 28],
[28, 32], [32, 36], [36, 40]]
prep.OpenBMI.fbcsp.subject_dependent_setting(k_folds=5,
pick_smp_freq=100,
n_components=4,
bands=bands,
n_features=8,
order=5,
save_path='datasets')
Subject-independent setting
Preprocess raw time-series EEG in subject-independent setting using Filter Bank Common Spatial Pattern (FBCSP). Split data into train, validation and test sets using stratified k-fold cross-validation.
fbcsp.subject_independent(k_folds,
pick_smp_freq,
n_components,
bands,
n_features,
order,
save_path,
num_class,
sel_chs)
Arguments:
| Arguments | Description |
|---|---|
| k_folds | int number of k-fold cross-validation. |
| pick_smp_freq | int pick sample frequency (downsampling EEG to to pick_smp_freq) |
| n_components | int number of components of CSP |
| bands | list list of low cut and high cut frequency bands of filter bank e.g. [[4, 8], [8, 12], ...] |
| n_features | int number of features for mutual_info_classif |
| order | int number of order of butter bandpass filter |
| save_path | str path to save processed EEG |
| num_class | int number of classes. Default 2 |
| sel_chs | list or None. list if EEG channels. Default None |
Example
import mixnet.preprocessing as prep
bands = [[4, 8], [8, 12], [12, 16],
[16, 20], [20, 24], [24, 28],
[28, 32], [32, 36], [36, 40]]
prep.OpenBMI.fbcsp.subject_independent_setting(k_folds=5,
pick_smp_freq=100,
n_components=4,
bands=bands,
n_features=8,
order=5,
save_path='datasets')
Spectral Spatial mapping
Subject-dependent setting
Preprocess raw time-series EEG in subject-dependent setting using Spectral Spatial mapping. Split data into train, validation and test sets using stratified k-fold cross-validation.
spectral_spatial.subject_dependent(k_folds,
pick_smp_freq,
n_components,
bands,
n_features,
order,
save_path,
num_class,
sel_chs)
Arguments:
| Arguments | Description |
|---|---|
| k_folds | int number of k-fold cross-validation. |
| pick_smp_freq | int pick sample frequency (downsampling EEG to to pick_smp_freq) |
| n_components | int number of components of CSP |
| bands | list list of low cut and high cut frequency bands of filter bank e.g. [[4, 8], [8, 12], ...] |
| n_pick_bands | int number of filter bands |
| n_features | int number of features for mutual_info_classif |
| order | int number of order of butter bandpass filter |
| save_path | str path to save processed EEG |
| num_class | int number of classes. Default 2 |
| sel_chs | list or None. list if EEG channels. Default None |
Example
import mixnet.preprocessing as prep
bands = [[7.5,14],[11,13],[10,14],[9,12],[19,22],
[16,22],[26,34],[17.5,20.5],[7,30],[5,14],
[11,31],[12,18],[7,9],[15,17],[25,30],
[20,25],[5,10],[10,25],[15,30],[10,12],
[23,27],[28,32],[12,33],[11,22],[5,8],
[7.5,17.5],[23,26],[5,20],[5,25],[10,20]]
prep.OpenBMI.spectral_spatial.subject_dependent_setting(k_folds=5,
pick_smp_freq=100,
n_components=10,
bands=bands,
n_pick_bands=20,
order=5,
save_path='datasets')
Subject-independent setting
Preprocess raw time-series EEG in subject-independent setting using Spectral Spatial mapping. Split data into train, validation and test sets using stratified k-fold cross-validation.
spectral_spatial.subject_independent(k_folds,
pick_smp_freq,
n_components,
bands,
n_features,
order,
save_path,
num_class,
sel_chs)
Arguments:
| Arguments | Description |
|---|---|
| k_folds | int number of k-fold cross-validation. |
| pick_smp_freq | int pick sample frequency (downsampling EEG to to pick_smp_freq) |
| n_components | int number of components of CSP |
| bands | list list of low cut and high cut frequency bands of filter bank e.g. [[4, 8], [8, 12], ...] |
| n_pick_bands | int number of filter bands |
| n_features | int number of features for mutual_info_classif |
| order | int number of order of butter bandpass filter |
| save_path | str path to save processed EEG |
| num_class | int number of classes. Default 2 |
| sel_chs | list or None. list if EEG channels. Default None |
Example
import mixnet.preprocessing as prep
bands = [[7.5,14],[11,13],[10,14],[9,12],[19,22],
[16,22],[26,34],[17.5,20.5],[7,30],[5,14],
[11,31],[12,18],[7,9],[15,17],[25,30],
[20,25],[5,10],[10,25],[15,30],[10,12],
[23,27],[28,32],[12,33],[11,22],[5,8],
[7.5,17.5],[23,26],[5,20],[5,25],[10,20]]
prep.OpenBMI.spectral_spatial.subject_independent_setting(k_folds=5,
pick_smp_freq=100,
n_components=10,
bands=bands,
n_pick_bands=20,
order=5,
save_path='datasets')
Spectral Spatial Signals generation
Subject-dependent setting
Preprocess raw time-series EEG in subject-dependent setting using Spectral Spatial Signals generation. Split data into train, validation and test sets using stratified k-fold cross-validation.
spectral_spatial_signals.subject_dependent(k_folds,
pick_smp_freq,
n_components,
bands,
n_features,
order,
save_path,
num_class,
sel_chs)
Arguments:
| Arguments | Description |
|---|---|
| k_folds | int number of k-fold cross-validation. |
| pick_smp_freq | int pick sample frequency (downsampling EEG to to pick_smp_freq) |
| n_components | int number of components of CSP |
| bands | list list of low cut and high cut frequency bands of filter bank e.g. [[4, 8], [8, 12], ...] |
| n_features | int number of features for mutual_info_classif |
| order | int number of order of butter bandpass filter |
| save_path | str path to save processed EEG |
| num_class | int number of classes. Default 2 |
| sel_chs | list or None. list if EEG channels. Default None |
Example
import mixnet.preprocessing as prep
bands = [[4, 8], [8, 12], [12, 16],
[16, 20], [20, 24], [24, 28],
[28, 32], [32, 36], [36, 40]]
prep.OpenBMI.spectral_spatial_signals.subject_dependent_setting(k_folds=5,
pick_smp_freq=100,
n_components=4,
bands=bands,
order=5,
num_class=2,
save_path='datasets')
Subject-independent setting
Preprocess raw time-series EEG in subject-independent setting using Spectral Spatial Signals generation. Split data into train, validation and test sets using stratified k-fold cross-validation.
spectral_spatial_signals.subject_independent(k_folds,
pick_smp_freq,
n_components,
bands,
n_features,
order,
save_path,
num_class,
sel_chs)
Arguments:
| Arguments | Description |
|---|---|
| k_folds | int number of k-fold cross-validation. |
| pick_smp_freq | int pick sample frequency (downsampling EEG to to pick_smp_freq) |
| n_components | int number of components of CSP |
| bands | list list of low cut and high cut frequency bands of filter bank e.g. [[4, 8], [8, 12], ...] |
| n_features | int number of features for mutual_info_classif |
| order | int number of order of butter bandpass filter |
| save_path | str path to save processed EEG |
| num_class | int number of classes. Default 2 |
| sel_chs | list or None. list if EEG channels. Default None |
Example
import mixnet.preprocessing as prep
bands = [[4, 8], [8, 12], [12, 16],
[16, 20], [20, 24], [24, 28],
[28, 32], [32, 36], [36, 40]]
prep.OpenBMI.spectral_spatial_signals.subject_independent_setting(k_folds=5,
pick_smp_freq=100,
n_components=2,
bands=bands,
order=5,
num_class=2,
save_path='datasets')