Brain-computer interfacing in amyotrophic lateral sclerosis: Implications of a resting-state EEG analysis

Despite decades of research on EEG-based brain-computer interfaces (BCIs) in patients with amyotrophic lateral sclerosis (ALS), there is still little known about how the disease affects the electromagnetic field of the brain. This may be one reason for the present failure of EEG-based BCI paradigms for completely locked-in ALS patients. In order to help understand this failure, we have recorded resting state data from six ALS patients and thirty-two healthy controls to investigate for group differences. While similar studies have been attempted in the past, none have used high-density EEG or tried to distinguish between physiological and non-physiological sources of the EEG. We find an ALS-specific global increase in gamma power (30-90 Hz) that is not specific to the motor cortex, suggesting that the mechanism behind ALS affects non-motor cortical regions even in the absence of comorbid cognitive deficits.

[1]  Katja Kollewe,et al.  Changes of resting state brain networks in amyotrophic lateral sclerosis , 2009 .

[2]  T. Demiralp,et al.  Human EEG gamma oscillations in neuropsychiatric disorders , 2005, Clinical Neurophysiology.

[3]  F. Esposito,et al.  Subcortical motor plasticity in patients with sporadic ALS: An fMRI study , 2006, Brain Research Bulletin.

[4]  R. Mai,et al.  Quantitative electroencephalography in amyotrophic lateral sclerosis. , 1998, Electroencephalography and clinical neurophysiology.

[5]  Ben Schmand,et al.  The ALS-FTD-Q , 2012, Neurology.

[6]  Christian Burkhardt,et al.  The Edinburgh Cognitive and Behavioural Amyotrophic Lateral Sclerosis Screen: A cross-sectional comparison of established screening tools in a German-Swiss population , 2015, Amyotrophic lateral sclerosis & frontotemporal degeneration.

[7]  T. Sejnowski,et al.  Removing electroencephalographic artifacts by blind source separation. , 2000, Psychophysiology.

[8]  B. Schölkopf,et al.  A brain–computer interface based on self-regulation of gamma-oscillations in the superior parietal cortex , 2014, Journal of neural engineering.

[9]  J. Wolpaw,et al.  Patients with ALS can use sensorimotor rhythms to operate a brain-computer interface , 2005, Neurology.

[10]  K. Priftis,et al.  Brain–computer interfaces in amyotrophic lateral sclerosis: A metanalysis , 2015, Clinical Neurophysiology.

[11]  J. Polich,et al.  EEG and ERP assessment of normal aging. , 1997, Electroencephalography and clinical neurophysiology.

[12]  J. Wolpaw,et al.  A P300-based brain–computer interface for people with amyotrophic lateral sclerosis , 2008, Clinical Neurophysiology.

[13]  Steven C.R. Williams,et al.  Cortical activation during motor imagery is reduced in Amyotrophic Lateral Sclerosis , 2007, Brain Research.

[14]  S. Muthukumaraswamy,et al.  Functional and structural correlates of the aging brain: Relating visual cortex (V1) gamma band responses to age‐related structural change , 2012, Human brain mapping.

[15]  R Verleger,et al.  Selective attention is impaired in amyotrophic lateral sclerosis--a study of event-related EEG potentials. , 1999, Brain research. Cognitive brain research.

[16]  S. Makeig,et al.  Imaging human EEG dynamics using independent component analysis , 2006, Neuroscience & Biobehavioral Reviews.

[17]  A Kübler,et al.  A P 300-based brain-computer interface for people with amyotrophic lateral sclerosis , 2010 .

[18]  Andrew Simmons,et al.  Altered cortical activation during a motor task in ALS , 2000, Journal of Neurology.

[19]  D Bottger Amplitude differences of evoked alpha and gamma oscillations in two different age groups , 2002 .

[20]  L. Rowland,et al.  Amyotrophic Lateral Sclerosis , 1980, Neurology.

[21]  Sneh Anand,et al.  Quantitative EEG analysis for assessment to 'plan' a task in amyotrophic lateral sclerosis patients: a study of executive functions (planning) in ALS patients. , 2004, Brain research. Cognitive brain research.

[22]  H. Flor,et al.  A spelling device for the paralysed , 1999, Nature.

[23]  Eric Moulines,et al.  A blind source separation technique using second-order statistics , 1997, IEEE Trans. Signal Process..