Multiclass classification method for the estimation of eeg signal quality

A method for assessing the quality of an electroencephalographic signal based on a multiclass classification, wherein said method comprises:

• receiving at least one segment of electroencephalographic signal acquired from at least one electrode;
• extracting at least one feature value from each channel of the electroencephalographic signal segment;
• classifying with a first classification so as to assign each channel of the electroencephalographic signal segment to one of at least three quality classes:
• {TAG1, TAG2, . . . , TAGN};
• wherein said first classification is performed by a k-nearest neighbors' algorithm:
• using a first training set comprising multiples training samples, wherein each training sample of the first training set is associated to one of the quality classes and to at least one feature value;
• assigning to each channel of the electroencephalographic signal segment the quality class which is the most frequent class among the k training samples of the first training set which are nearer to each channel of the electroencephalographic signal segment;
• wherein the distance is calculated between the feature value of each channel of the electroencephalographic signal segment and each feature value of the training samples;
• and
• outputting said quality class for each channel of the electroencephalographic signal segment.

A system for assessing the quality of an electroencephalographic signal (EEG) based on a multiclass classification, said system comprising a data processing system having:

• at least one input adapted to receive at least one segment of electroencephalographic signal acquired from at least one electrode;
• at least one processor configured to iteratively:
• extracting at least one feature value from each channel of the electroencephalographic signal segment;
• classifying with a first classification so as to assign each channel of the electroencephalographic signal segment to one of at least three quality classes:
• {TAG1, TAG2, . . . , TAGN};
• wherein said first classification is performed by a k-nearest neighbors' algorithm:
• using a first training set comprising multiples training samples, wherein each training sample of the first training set is associated to one of the quality classes and to at least one feature value;
• and
• assigning to each channel of the electroencephalographic signal segment the quality class which is the most frequent class among the k training samples of the first training set which are nearer to each channel of the electroencephalographic signal segment;
• wherein the distance is calculated between the feature value of each channel of the electroencephalographic signal segment and each feature value of the training samples;
• at least one output adapted to provide said quality class for each channel of the electroencephalographic signal segment.